DEV Community: David Thomas

Build a Raspberry Pi Face Detection System Without Training Any AI Models

David Thomas — Sat, 20 Jun 2026 10:41:20 +0000

Machine learning projects usually start with collecting datasets, labeling images, training models, and spending hours waiting for everything to work.

That's exactly why many engineering students avoid computer vision projects in the beginning.

This Raspberry Pi Face Detection project takes a completely different approach. Instead of running AI models locally on the Raspberry Pi, the heavy lifting is handled in the cloud. All the Raspberry Pi needs to do is capture an image and send it for processing. The result comes back in seconds with the number of detected faces and confidence scores.

Why This Project Is Different

Most face detection tutorials focus on model training or deploying pre-trained networks on the device itself.

While that's great for learning machine learning, it can become frustrating when you're simply trying to build something functional. Here, the Raspberry Pi acts as the bridge between the camera and a cloud-based Face Detection API.

The setup is surprisingly simple. A USB webcam captures frames, OpenCV handles image processing, and the cloud service performs face detection.

Hardware You'll Need

One reason I like this project is that the hardware requirements are minimal.

You'll need a Raspberry Pi, a USB camera, and a microSD card with Raspberry Pi OS installed. That's pretty much it.

No AI accelerator boards. No GPU. No complicated wiring.

How the Face Detection Workflow Works

The workflow is straightforward and easy to understand.

The USB camera continuously captures images while OpenCV displays the live feed. When an image is captured, it gets converted into JPEG format and sent securely to a cloud-based Face Detection API using HTTPS. The server analyzes the image and sends back the detection result, including the number of faces identified.

From the Raspberry Pi's perspective, it's simply sending an image and receiving a response.

Setting Up the Raspberry Pi

Before running the code, make sure Raspberry Pi OS is installed and your Pi is connected to the internet.

You can work directly with a monitor and keyboard, connect remotely using SSH, or access the full desktop using VNC Viewer. Personally, VNC is usually the most convenient option because it gives you access to both the desktop and terminal from your laptop.

The Role of OpenCV

OpenCV handles all camera operations in this project.

It initializes the webcam, captures images, converts frames into JPEG format, and provides a live preview window. Without OpenCV, you'd have to handle camera communication manually, which would make the project much more complicated.

The best part is that OpenCV runs smoothly even on a standard Raspberry Pi setup.

Multiple Capture Modes

One feature I found particularly useful is the availability of multiple operating modes.

Keyboard mode allows you to press the spacebar to capture an image manually. Auto mode captures images automatically at fixed intervals, making it suitable for monitoring applications. SSH mode works entirely from the terminal without displaying a video window, which is useful for headless deployments.

This flexibility makes the project useful for both learning and practical applications.

Real-World Applications

Face detection isn't just for fancy demos.

A project like this can be used for attendance systems, visitor logging, people counting, security monitoring, and educational demonstrations. Because the cloud handles the AI processing, students can focus more on building the overall system rather than spending time training models.

That's often where the most valuable learning happens.

What Makes It Great for Students

Many engineering students want to explore AI but get stuck at the model training stage.

This project removes that barrier completely. You still learn about computer vision, APIs, image processing, networking, and Raspberry Pi programming, but without the complexity of building and optimizing machine learning models from scratch.

For a weekend project, that's a pretty good trade-off.

What I like most about this setup is how quickly you can go from an idea to a working prototype.

Within a short time, you can have a Raspberry Pi capturing images, sending them to the cloud, and detecting faces in real time. It feels like one of those projects that introduces several important engineering concepts at once while still being simple enough for beginners to complete successfully.

Raspberry Pi Project, IoT Project, AI Project

I Opened Up an ESP32 Smart Display to See What's Inside: ESP32 2424S012C Teardown

David Thomas — Fri, 19 Jun 2026 10:38:53 +0000

Development boards usually hide all the interesting engineering under a neat enclosure.

Most of us connect a USB cable, upload some code, and start building projects without thinking much about what's happening underneath. But every now and then, it's worth taking a closer look at the hardware itself.

That's exactly what I did with the ESP32-2424S012C, a compact ESP32-C3 development board that comes with a built-in 1.28-inch IPS capacitive touch display. At first glance, it looks like a tiny smart display module. After opening it up, I realized there's a lot of clever engineering packed into this small board.

A Tiny Board Packed With Features: ESP32 2424S012C Teardown

The first thing that stands out is how much functionality is squeezed into such a compact design.

The board combines an ESP32-C3 microcontroller, a circular 240×240 IPS display, capacitive touch support, battery charging circuitry, voltage regulation, and USB programming hardware. Instead of connecting multiple modules with jumper wires, everything is integrated onto a single PCB.

For anyone who has spent hours wiring displays to ESP32 boards on a breadboard, this immediately feels like a cleaner solution.

Looking at the Main PCB

Once the display assembly is removed, the main components become easier to identify.

At the center sits the ESP32-C3, which handles all processing and wireless communication. Nearby are the boot, reset, and power buttons, along with a JST battery connector for connecting a 3.7V lithium battery.

The board also includes an IP5306 power management IC. This chip handles battery charging while simultaneously boosting the battery voltage when needed.

Another interesting component is the A6165P voltage regulator, which converts the available 5V rail into a stable 3.3V supply required by the ESP32 and display electronics.

The Display Is More Interesting Than It Looks

The display assembly itself contains some fascinating design choices.

Instead of using a traditional display controller mounted on a separate PCB, the display uses a GC9A01 driver implemented using Chip-On-Glass (COG) technology. The controller IC is bonded directly onto the display glass, reducing size and improving overall integration.

If you look closely, you'll notice a tiny black strip attached directly to the glass. That's actually the display controller.

The touch controller uses a different approach called Chip-On-Flex (COF), where the controller is mounted directly onto the flexible cable that connects the display to the main board.

It's one of those details that you rarely notice until you take the hardware apart.

Understanding the Display Connections

A single 16-pin FPC cable connects the display assembly to the main PCB.

These pins handle power delivery, LED backlight control, SPI communication for the display, and I²C communication for the touch controller.

The display uses SPI because graphical displays require fast data transfer speeds. The touch controller uses I²C because touch data requires far less bandwidth.

It's a simple design, but it's optimized well.

Battery Management Done Right

One thing I appreciate about this board is that it was clearly designed with portable projects in mind.

The IP5306 chip allows a lithium battery to be connected directly to the board. It manages charging while also providing a boosted output voltage when required.

This means you can build battery-powered projects such as smart watches, control panels, portable dashboards, or touch-based controllers without adding extra power management hardware.

For makers, that's a huge convenience.

USB Programming and Expansion Options

Programming the board is straightforward thanks to the USB Type-C connector.

The USB data lines connect directly to the ESP32-C3, allowing code uploads without additional adapters. There's also a dedicated UART connector available for alternative programming and debugging methods.

Having both options available makes development much easier, especially when troubleshooting firmware issues.

Why This Board Is Popular Among Makers

After looking through the hardware design, it becomes easy to understand why these smart display modules are becoming so popular.

They eliminate much of the wiring normally required when building graphical ESP32 projects. The display, touch controller, battery charging circuit, and microcontroller are already integrated and tested together.

That means less time troubleshooting hardware and more time building actual applications.

Teardowns are always a good reminder that even simple-looking development boards contain a surprising amount of engineering.

The ESP32-2424S012C combines display technology, power management, touch sensing, USB communication, and wireless processing into a remarkably compact package. For students learning embedded systems, exploring hardware like this is often just as educational as writing code.

And honestly, once you've seen how much functionality fits inside a board this small, you'll never look at a smart display module the same way again.

PCB Projects, Product Reviews

Build an ESP32 GSM Voice Calling Device with Auto Answer Using SIM868

David Thomas — Thu, 18 Jun 2026 10:29:33 +0000

Most IoT projects focus on sending sensor data to the cloud. That's useful, but sometimes a simple phone call can be far more effective.

Imagine an emergency button that instantly calls a family member, a security device that automatically answers incoming calls, or a wireless intercom system that works without Wi-Fi. That's exactly what this project achieves using an ESP32 and a GSM network.

In this ESP32 GSM voice Calling Device build, we create a compact ESP32 GSM Voice Calling Device that can place a phone call with the press of a button and automatically answer incoming calls. The best part is that everything is built around a single board, making the setup surprisingly simple.

Why This Project Is Interesting

Instead of displaying information on a screen, the device lets people communicate directly through a speaker and microphone. It instantly feels more practical and closer to a real product.

The project uses the GeoLinker GL868 development board, which combines an ESP32-S3 and a SIM868 GSM module on the same PCB. This removes the need for complicated wiring between separate GSM and microcontroller modules.

How the System Works

The logic is straightforward.

A push button is connected to the ESP32. When the button is pressed, the ESP32 sends AT commands to the SIM868 modem, instructing it to dial a predefined phone number. The GSM network then handles the call just like a normal mobile phone.

For incoming calls, the process works in reverse.

Whenever the SIM868 detects an incoming call, it continuously sends a "RING" notification to the ESP32. The microcontroller immediately responds with the ATA command, automatically answering the call and enabling two-way communication through the connected speaker and microphone.

Hardware Requirements

The setup consists of the GeoLinker GL868 board, a small speaker, a condenser microphone, a push button, a 3.7V Li-ion battery, and a GSM SIM card with voice support.

That's enough to build a fully functional wireless communication device.

Understanding the SIM868 AT Commands

If you've never worked with GSM modules before, this project is a good introduction.

The ESP32 communicates with the SIM868 using standard AT commands. Commands such as ATD initiate a phone call, ATA answers incoming calls, and ATH disconnects an active call. Additional commands configure microphone gain, speaker volume, and audio routing.

Once you understand these commands, building more advanced GSM projects becomes much easier.

Challenges During Development

Like most GSM projects, power management is important.

The modem draws significant current while initiating calls. If the battery cannot supply enough current, calls may fail or disconnect unexpectedly. Using a fully charged lithium battery solves most of these issues.

Audio configuration can also be tricky. Repeatedly sending audio-related AT commands during a call may result in distorted or broken audio. Proper call-state management helps prevent this problem.

Real-World Applications

What makes this project exciting is how easily it can be adapted.

The same design can be used as an emergency calling device for elderly people, an industrial alert system, a wireless intercom, or a GSM-based security communication system.

With a few modifications, multiple buttons can even be added to call different phone numbers, making it suitable for larger communication networks.

What Engineering Students Can Learn

This project teaches much more than simply making phone calls.

You'll gain experience with GSM communication, AT commands, serial communication, battery-powered embedded systems, and real-time event handling. These are practical skills that appear frequently in IoT and industrial automation projects.

More importantly, you'll understand how cellular communication works beyond sending SMS messages.

Building an ESP32 GSM Voice Calling Device is a refreshing change from typical IoT projects. Instead of pushing data to a dashboard, you're creating a device that enables direct voice communication through the GSM network.

For students exploring embedded systems, IoT, and wireless communication, this project offers a hands-on way to learn how cellular technology can be integrated into real-world applications while keeping the hardware simple and approachable.

ESP32 Project, IoT Project

Build a ESP32-CAM Face Detection Without AI Training

David Thomas — Thu, 18 Jun 2026 07:12:11 +0000

Face detection is one of those technologies that looks complicated from the outside. Many engineering students assume they need machine learning knowledge, large datasets, and powerful hardware before they can build something similar.

The good news is that isn't true anymore.

With an ESP32-CAM and a cloud-based face detection API, you can build a working ESP32-CAM Face Detection Project in just a few steps. No model training, no dataset collection, and no advanced AI setup required.

Why ESP32-CAM is a Great Choice for Face Detection Projects

The ESP32-CAM is one of the most popular boards among makers and engineering students because it combines Wi-Fi and a camera in a compact package.

Instead of spending money on expensive development kits, you can experiment with computer vision concepts using a low-cost board that fits comfortably on a breadboard.

For students working on mini-projects or final-year prototypes, that's a huge advantage.

How This ESP32-CAM Face Detection System Works

The concept behind this project is simple.

A push button is connected to the ESP32-CAM. Whenever the button is pressed, the camera captures an image and uploads it through Wi-Fi to a cloud-based face detection service.

The server processes the image and returns the detection results. The ESP32-CAM then displays the number of faces detected along with the confidence score through the Serial Monitor.

This approach removes the need to run complex image-processing algorithms directly on the microcontroller.

Hardware Required for the Face Detection System

One of the best things about this project is the minimal hardware requirement.

You only need:

ESP32-CAM Module
Push Button
Breadboard
Jumper Wires

If you're using a standard ESP32-CAM without onboard USB support, you'll also need an FTDI programmer for uploading the code.

That's enough to get the entire system working.

Why Cloud-Based Face Detection Makes Sense

Running face detection locally requires significant memory and processing power.

While the ESP32-CAM is powerful for its size, it isn't designed to handle advanced computer vision algorithms efficiently. Offloading the heavy processing to the cloud allows the microcontroller to focus on capturing and transmitting images.

The result is a faster and more reliable system without increasing hardware costs.

For students, it also means spending more time building projects and less time training AI models.

Improving Face Detection Accuracy

During testing, one thing became obvious.

Image quality matters.

A well-lit image with a clear view of the face produces much better results than a dark or blurry image. Poor lighting can reduce confidence scores and sometimes prevent faces from being detected altogether.

If you're planning to deploy this system, proper camera placement and lighting should be your first priority.

Real-World Applications of ESP32-CAM Face Detection

Once the basic project is working, there are plenty of ways to expand it.

You can integrate it into a smart doorbell that detects visitors before sending notifications. It can also be used in attendance monitoring systems where the system counts the number of people entering a classroom.

Retail visitor counters, security monitoring systems, and smart alert applications are also possible extensions.

The project serves as a strong foundation for more advanced computer vision applications.

Challenges You May Encounter

Like most IoT projects, this one has a few limitations.

The system depends on an internet connection because image processing happens in the cloud. If Wi-Fi connectivity is unstable, image uploads may fail.

Detection accuracy can also be affected by poor image quality, low lighting conditions, or partially visible faces.

Despite these limitations, the setup remains much simpler than building and training a custom AI model from scratch.

This project goes beyond face detection.

While building it, you'll learn about ESP32-CAM programming, image capture, cloud APIs, HTTPS communication, JSON responses, and IoT integration.

These are practical skills that appear frequently in modern embedded and IoT projects.

More importantly, you'll get hands-on experience with a real-world computer vision application without needing advanced machine learning knowledge.

The ESP32-CAM Face Detection Project is a great example of how modern cloud services can simplify complex tasks. Instead of worrying about datasets, model training, and optimization, you can focus on understanding how the system works and building useful applications around it.

For engineering students looking to explore computer vision, IoT, and embedded systems, this project is an excellent place to start. It delivers quick results, teaches valuable concepts, and opens the door to many more advanced projects in the future.

ESP32 Projects, IoT Projects, AI Projects

Building a Smart Waste Detection System with ESP32-CAM

David Thomas — Sat, 13 Jun 2026 10:53:24 +0000

Walk past any public dustbin and you'll probably notice the same thing - food waste, plastic bottles, paper cups, and wrappers all mixed together.

The problem isn't collecting waste anymore. The real challenge is sorting it correctly. When biodegradable and non-biodegradable waste get mixed, recycling becomes harder and a large portion of the waste ends up in landfills instead.

As engineering students, we've all seen projects that try to solve this problem using sensors. But I wanted to explore something more practical: what if a camera could simply look at a piece of waste and identify its category automatically?

That's exactly what this ESP32-CAM waste detection project does.

Why I Chose ESP32-CAM for This Project

Computer vision projects often sound expensive.

Most people immediately think about Raspberry Pi boards, GPUs, or dedicated AI hardware. But the ESP32-CAM changes that perception completely.

For a few dollars, you get a microcontroller, Wi-Fi connectivity, and a camera module in a single board. That makes it one of the easiest ways to start experimenting with image-based AI projects.

For student projects, that's a huge advantage.

Hardware Required

One of the best parts of this build is its simplicity.

The hardware consists of:

ESP32-CAM
Push button
Red LED
Green LED
Breadboard and resistors

That's it. No external processors, no dedicated AI hardware, and no complex sensor networks. :contentReference[oaicite:2]{index=2}

Smart Waste Detection System

The workflow is surprisingly simple.

When the push button is pressed, the ESP32-CAM captures an image of the waste item placed in front of it. The image is then sent through Wi-Fi to a cloud AI service for analysis.

Within a few seconds, the system receives a response indicating whether the object belongs to the biodegradable or non-biodegradable category.

To make the output easy to understand, LEDs provide instant visual feedback. One LED indicates biodegradable waste, while the other indicates non-biodegradable waste.

The Smart Part Happens in the Cloud

One thing I really liked about this project is that there was no need to train a machine learning model.

Anyone who has worked with TensorFlow or Edge Impulse knows that collecting datasets, labeling images, training models, and optimizing them can take days or even weeks.

Instead of doing all that, the ESP32-CAM simply uploads an image and receives the classification result through an API. This keeps the hardware lightweight while still delivering AI-powered functionality.

What I Learned During Testing

Getting the first successful detection felt great.

But I quickly realized that image quality plays a major role in accuracy. Poor lighting, shadows, and blurry images can affect the classification result significantly.

After a few tests, proper lighting and a stable camera position made the system much more reliable. It's one of those small details that makes a big difference in computer vision projects.

Real-World Applications Beyond a Classroom Project

What started as a simple experiment can actually be expanded into something much larger.

Smart dustbins, automated recycling systems, public waste collection centers, and educational demonstrations can all benefit from image-based waste classification.

With additional hardware, the system could even be integrated with automated sorting mechanisms that physically separate waste into different containers.

Many AI projects seem intimidating because they involve machine learning, datasets, and complex deployment pipelines.

This project shows that you can still build something genuinely useful without getting buried in model training. It combines embedded systems, Wi-Fi communication, cloud computing, and computer vision in a way that's easy to understand and surprisingly fun to build.

And honestly, seeing a tiny ESP32-CAM identify waste categories in real time feels pretty satisfying.

ESP32 Project, AI Project

Building an ESP32-CAM Indian Currency Recognition System for the Visually Impaired

David Thomas — Thu, 11 Jun 2026 12:10:07 +0000

Handling cash is something most of us do without thinking. But for visually impaired people, identifying currency notes can be a daily challenge.

Many people learn to recognize notes by touch, but that becomes harder with age. An elderly shopkeeper or street vendor with weak eyesight may struggle to verify the denomination handed over by customers. A simple mistake can lead to financial loss.

To address this problem, I built an ESP32-CAM Indian Currency Recognition. The idea is simple: point a camera at a currency note, press a button, and hear the denomination announced through a speaker.

The Idea Behind the Project

The system uses an ESP32-CAM to capture an image of an Indian currency note.

Instead of running a machine learning model directly on the microcontroller, the captured image is sent to a cloud-based AI service. The cloud processes the image, identifies the denomination, and sends the result back to the ESP32-CAM. The system then converts the result into speech and announces it through a speaker.

The entire process takes only a few seconds.

Why Use ESP32-CAM?

The ESP32-CAM is one of the most affordable boards available for computer vision projects.

It combines Wi-Fi connectivity, a camera interface, onboard processing, and compact size into a single board. This makes it perfect for projects that need image capture without requiring expensive hardware.

For student projects, it is often the easiest way to get started with AI-powered vision applications.

Hardware Required

The hardware setup is surprisingly simple.

You only need:

ESP32-CAM
PAM8403 audio amplifier
Speaker
Push button

The push button triggers image capture, the ESP32-CAM handles image transmission, and the speaker announces the detected denomination.

How the System Works

The workflow is straightforward.

First, the user places a currency note in front of the camera and presses the button. The ESP32-CAM captures a high-quality image and uploads it to the cloud recognition API. The cloud analyzes the note and returns the detected denomination.

Once the result arrives, the system generates audio using text-to-speech and plays it through the speaker. Instead of reading a display, the user simply listens to the spoken output.

This makes the system far more accessible for visually impaired users.

Why CircuitDigest Cloud Instead of Running ML on the ESP32?

This was one of the most interesting parts of the project.

Traditional machine learning workflows usually require collecting hundreds of images, labeling datasets, training models, optimizing them, and then deploying them to embedded hardware.

That process can take days.

In this project, the heavy lifting happens in the cloud. The ESP32-CAM only captures and uploads images. The recognition model is already trained and maintained on the server side, making development much faster.

For beginners, this removes one of the biggest barriers to building AI-powered projects.

Audio Feedback Makes a Huge Difference

The recognition itself is useful, but the audio output is what makes the system practical.

After identifying the note, the ESP32-CAM uses a text-to-speech service to generate spoken audio. The amplifier boosts the sound, and the speaker announces the denomination clearly.

This means users don't need a display or smartphone screen to understand the result.

Challenges I Faced

Like most vision-based projects, image quality matters.

Poor lighting, folded notes, camera shake, or blurry images can affect recognition accuracy. During testing, keeping the note around 10–15 cm from the camera and using proper lighting produced the most reliable results.

A stable Wi-Fi connection is also important since the image must be uploaded before processing can begin.

Where This Can Be Used

While this project was designed for visually impaired users, there are several other use cases.

It can be used in small shops, educational demonstrations, assistive devices, banking kiosks, or even as part of a larger financial accessibility system.

The same concept can also be expanded to recognize foreign currencies, documents, or everyday objects.

What I like most about this project is that it combines embedded systems, cloud AI, computer vision, and accessibility into a single build.

The hardware is simple, the setup is beginner-friendly, and the result solves a real-world problem. More importantly, it shows how affordable hardware like the ESP32-CAM can be used to create practical assistive technology that genuinely helps people become more independent.

IoT Projects, ESP32 Projects, AI Project

How I Created a Touchscreen GUI on ESP32-C3-based Smartwatch with LVGL

David Thomas — Wed, 03 Jun 2026 06:14:50 +0000

Most engineering students spend a lot of time learning sensors, communication protocols, and microcontrollers. But when it comes to creating a professional-looking user interface, things often get stuck at plain text displays and basic graphics.

That’s where LVGL comes in.

LVGL (Light and Versatile Graphics Library) makes it possible to create modern touch-enabled interfaces on microcontrollers like the ESP32. Think buttons, sliders, animations, dashboards, and even smartwatch-style screens—all running on embedded hardware.

In this project we are getting started with Arduino LVGL for ESP32 Display, this ESP32-C3-based smartwatch development board is used to demonstrate how easy it is to build a custom GUI using LVGL and the Arduino IDE.

Why Learn LVGL?

There is a huge difference between a project that works and a project that feels polished.

A temperature monitoring system with plain text gets the job done. A temperature monitoring system with touch controls, animated buttons, and a clean dashboard feels like an actual product.

That's exactly why many developers use LVGL for embedded applications such as smart home dashboards, wearable devices, industrial displays, and automation panels.

What Makes LVGL Popular?

One reason LVGL has gained so much popularity is its flexibility.

It supports a wide range of microcontrollers, display types, and touch interfaces while remaining lightweight enough to run on resource-constrained hardware. The library is open source, actively maintained, and available directly through the Arduino Library Manager.
Another advantage is the huge collection of ready-made widgets.

Instead of drawing every element manually, you can simply create buttons, sliders, labels, charts, switches, and many other interface components with just a few lines of code.

The Hardware Used

For this demonstration, a compact ESP32-C3 round-display development board was used.

The board combines several components into a single package:

ESP32-C3 microcontroller
1.28-inch round LCD display
Capacitive touch controller
Battery charging circuitry

Since everything is integrated, there is no need for complicated wiring, making it perfect for experimenting with GUI development.

Building the First Interface

One of the nicest things about LVGL is that you don't always have to start from scratch.

The official documentation includes a large collection of widget examples. In this project, a simple button example was selected from the documentation and integrated into the Arduino sketch.

After uploading the code, the display shows interactive buttons with built-in animations.

A toggle button was also modified so that switching it ON changes the screen background color. Small details like this instantly make the interface feel more responsive and professional.

What Happens Behind the Scenes?

LVGL acts as a layer between your hardware and your application.

The display driver handles drawing pixels on the screen, while the touch driver detects user interaction. LVGL sits in the middle and manages widgets, events, rendering, and animations.

When a user taps a button, LVGL generates an event. Your application can then respond to that event and update the interface accordingly.

This event-driven approach keeps the code organized and easy to scale.

Learning LVGL gives you the ability to present your projects in a way that feels closer to commercial products. Whether you're building an IoT dashboard, a wearable device, a smart appliance, or a monitoring system, a good interface can completely change how people perceive your project.

Where to Go Next

Once you understand the basics of buttons and touch events, you can move on to more advanced widgets such as sliders, gauges, charts, and animated menus.

From there, creating smartwatch interfaces, smart home control panels, industrial dashboards, or portable handheld devices becomes much easier.

The best part is that the same LVGL concepts can be reused across different ESP32 boards and display modules, making it a skill that continues to pay off long after your first project.

ESP32 Project

Building a ESP32-CAM Helmet Detection System Using and CircuitDigest Cloud

David Thomas — Wed, 27 May 2026 11:28:47 +0000

Traffic monitoring sounds complicated until you realize a tiny ESP32-CAM can actually do most of the work.

This ESP32-CAM Helmet Detection project captures an image, uploads it to a cloud AI service, and checks whether riders are wearing helmets or not. The best part is that the ESP32 doesn’t run any heavy AI model locally, which makes the whole setup much simpler and cheaper to build.

For engineering students, this feels like one of those projects that actually looks impressive when it starts working in real time.

Why This Project Feels Different

Most ESP32 AI projects quickly become frustrating because of memory limitations and model deployment issues.

Here, the ESP32-CAM only handles:

image capture
WiFi connection
HTTPS upload
response handling

The cloud server handles the actual helmet detection.

That means no TensorFlow setup, no model training, and no painful optimization steps.

Honestly, that saves a lot of time.

How the Smart Helmet Detection System Works

The workflow is pretty smooth.

When powered ON, the green LED indicates the system is ready. After a few seconds, the ESP32-CAM captures an image and uploads it securely to the cloud API.

The cloud analyzes the image and returns:

riders with helmets
riders without helmets
number of motorbikes detected

The result then appears on the Serial Monitor, and a WhatsApp notification is sent instantly.

Getting a WhatsApp alert from your own ESP32 project feels surprisingly satisfying.

Hardware Used

The setup is very minimal:

ESP32-CAM
Red LED
Green LED
Breadboard
Jumper wires

That’s enough to build the complete system.

No Raspberry Pi.

No GPU board.

No external AI accelerator.

Which is exactly why this project is great for students experimenting with computer vision for the first time.

Future Improvements

This system can easily grow into:

smart traffic monitoring
automatic challan systems
parking security
campus safety systems
AI-based surveillance projects

For such a tiny setup, the possibilities become surprisingly huge.

IoT Project, ESP32 Projects, AI Project

ESP32 Into a Speech-to-Text Device

David Thomas — Fri, 22 May 2026 10:22:57 +0000

Typing commands into a serial monitor feels old once you start playing with voice interfaces.

So I decided to try something more interesting — building a small ESP32 Speech to Text system using an INMP441 I2S microphone and an OLED display. The setup listens to speech, sends audio to a cloud API, and converts spoken words into text almost instantly.

And honestly, seeing your own words appear live on a tiny OLED screen feels surprisingly futuristic for such a small project.

Why I Didn’t Use Offline Speech Recognition

At first, I thought about running everything directly on the ESP32.
Then reality hit.

Speech recognition models are heavy. The ESP32 simply doesn’t have enough processing power or memory to run large speech-to-text models locally in a reliable way. Instead of fighting hardware limitations for days, I used a cloud-based speech recognition service called Wit.ai.

The ESP32 only handles:

audio capture
WiFi communication
displaying results

The cloud handles the difficult AI processing.

Way simpler.

How This Project Works

The workflow is actually pretty clean.

The INMP441 microphone captures audio using the I2S protocol. The ESP32 records the audio as 16-bit PCM data and sends it over HTTPS to Wit.ai using WiFi.

Once processed, Wit.ai sends back the recognized text in JSON format.

The ESP32 extracts the text and displays it on:

OLED display
Serial Monitor

So the whole system behaves almost like a tiny voice assistant.
Press button → speak → get text.

Components Used

The hardware setup is very small:

ESP32 development board
INMP441 I2S microphone
0.91-inch OLED display
push button
jumper wires
breadboard

That’s it.

No extra audio shield.

No Raspberry Pi.

No expensive AI hardware.

Setting Up Wit.ai Was Easier Than Expected

I honestly expected cloud AI setup to be painful.
But the process was surprisingly simple:

Create a Wit.ai app
Copy the Service Access Token
Paste token into Arduino code

Done.

The ESP32 sends raw audio directly to:

api.wit.ai

using HTTPS requests.
No custom server setup required.

OLED Feedback Makes the Project Feel Alive

One thing I really liked was the OLED status updates.
The display switches between:

Connecting...
Ready
Listening...
Processing...

It makes the device feel interactive instead of just dumping logs into Serial Monitor.

Once the recognized text appears on the OLED, the project suddenly feels much more polished.

Future Upgrades I Want to Try

This setup can easily evolve into:

voice-controlled home automation
smart assistants
speech logging systems
WhatsApp voice notifications
MQTT-based voice dashboards

You could even combine it with text-to-speech later and create a complete two-way voice assistant using only ESP32 hardware.

For a small microcontroller project, this one feels surprisingly close to real-world AI systems.

AI Projects, ESP32 Projects,

AI Object Detection System Using ESP32-CAM and Cloud Vision API

David Thomas — Fri, 22 May 2026 07:12:39 +0000

Computer vision always looked complicated to me.

Every tutorial seemed to involve machine learning models, dataset training, TensorFlow setups, or huge Python environments that made things feel overwhelming very quickly. As engineering students, most of us just want to build something that works without spending weeks training AI models.

The idea is simple. Press a button, capture an image using the ESP32-CAM, send it to a cloud API, and instantly get object detection results back on the Serial Monitor.

What ESP32-CAM Object Detection Project Actually Means

The ESP32-CAM captures an image whenever the push button is pressed.

That image gets uploaded to a cloud object detection API where the processing happens. The server identifies objects inside the image and sends back:

object names
object count
confidence scores

The results are then displayed directly in the Arduino Serial Monitor.

Seeing labels like “Laptop,” “Phone,” or “Mouse” appear automatically after taking a picture honestly feels pretty satisfying.

Why This Project Feels Beginner Friendly

Most AI-based embedded projects usually fail at one point: setup complexity.

Collecting datasets, labeling images, training models, optimizing inference — it quickly becomes exhausting. This project removes all of that complexity because the heavy AI processing happens in the cloud instead of on the ESP32 itself.

That means the ESP32-CAM only handles:

image capture
Wi-Fi communication
sending HTTP requests

Which makes the whole workflow much easier to understand.

Hardware Required

The setup is extremely small:

ESP32-CAM
push button
breadboard
jumper wires

The push button acts as the trigger for capturing images. Once pressed, the camera captures a frame and uploads it for detection.

If you’re using the normal ESP32-CAM without onboard USB, you’ll also need an FTDI programmer for uploading code.

How the Workflow Happens

The process looks something like this:

User presses button
ESP32-CAM captures image
Image uploads through Wi-Fi
Cloud API processes image
Objects get detected
Detection results return to ESP32
Serial Monitor displays object names and confidence values

Everything happens within a few seconds.

And honestly, that response time feels surprisingly fast considering the ESP32 itself isn’t doing any actual AI inference locally.

The Most Important Thing: Good Lighting

One thing I learned quickly while testing this project:

Lighting matters A LOT.

If the room is dark or the image looks blurry, detection accuracy drops immediately. The cloud model can only analyze what the camera sees clearly.

After adjusting brightness and manually focusing the ESP32-CAM lens a bit, the results became much better.

Even small changes in image clarity made a huge difference.

Future Improvements

This setup can easily grow into larger projects:

smart surveillance systems
automated attendance
AI sorting machines
smart parking detection
inventory monitoring

And since the detection runs in the cloud, adding more object classes becomes much easier compared to training models manually.

Honestly, this project feels like one of the easiest ways to start experimenting with AI and embedded systems together without getting stuck in complicated ML workflows.

ESP32 Projects, AI Projects, IoT Projects

I Controlled an ESP32 Drone Using Only My Voice

David Thomas — Thu, 21 May 2026 12:20:02 +0000

Flying drones with joysticks is fun at first.

But after a while, every engineering student starts thinking the same thing:

“What if the drone could just listen to commands directly?”

That’s exactly what this project is about.

I built a voice controlled drone using an ESP32-based LiteWing drone, Python, and offline speech recognition. Instead of using a traditional remote controller, the drone responds to spoken commands like “takeoff,” “forward,” “left,” and “land.”

And the best part?

It works completely offline.

No cloud APIs.

No internet.

No voice assistants.

Just your voice controlling a flying machine in real time.

Why This Project Felt So Cool

Most of us have already built Bluetooth cars or Wi-Fi robots during college.

But controlling a drone using voice commands feels completely different. The first time the drone actually took off after saying “takeoff,” it honestly felt futuristic.

This project combines multiple things engineering students usually learn separately:

embedded systems
Python programming
networking
speech recognition
drone communication

And somehow all of them come together into one really fun build.

Hardware Setup Was Surprisingly Simple

The setup mainly uses:

LiteWing ESP32 drone
positioning module
laptop or PC
microphone

The laptop connects directly to the drone’s Wi-Fi access point. Voice commands are processed on the laptop and then sent wirelessly to the drone.

That means the ESP32 itself doesn’t perform speech recognition. The laptop handles all the heavy processing while the drone focuses on flying smoothly.

Honestly, this made development much easier.

The Brain of the System: Vosk Speech Recognition

For voice recognition, I used Vosk.

What made Vosk perfect for this project is that it works fully offline. Since the laptop directly connects to the drone’s Wi-Fi network, internet access usually disappears during operation. Online speech APIs would have completely failed here.

The workflow is simple:

Microphone captures audio
Vosk converts speech into text
Python checks for keywords
Commands get sent to the drone

The response feels surprisingly fast.

There’s very little delay between speaking and drone movement.

Supported Commands

The drone can understand commands like:

takeoff
land
forward
backward
left
right
up
down
turn left
turn right

I also added LED color commands because honestly… why not?

Saying “blue” and watching the drone LEDs instantly change color feels unnecessarily satisfying.

The Biggest Problem I Faced

Speech recognition accuracy.

Initially, I used the default American English Vosk model. The system kept misunderstanding commands because of accent differences. Sometimes “land” became random words, which is definitely not something you want while flying a drone.

Switching to the Indian English Vosk model improved accuracy massively. After that, command detection became much smoother and more reliable.

That small change honestly saved the whole project.

Drone Stability Matters More Than You Think

One thing I learned quickly:

Voice control is useless if the drone itself isn’t stable.

The positioning module played a huge role here. Without proper stabilization, the drone drifted too much during hover and movement commands. Updating the LiteWing firmware also improved flight stability a lot.

This project made me realize that good software still depends heavily on reliable hardware.

Future Improvements

There’s still a lot that can be added:

custom wake words
obstacle avoidance
multilingual support
gesture + voice hybrid control
autonomous navigation

The foundation is already there.

And honestly, once you start controlling drones with your voice, normal remotes suddenly feel boring.

Drone Projects, ESP32 Projects

IVR System Using ESP32-S3

David Thomas — Mon, 18 May 2026 11:56:48 +0000

Internet-based automation is everywhere now.

Turn ON lights with an app. Monitor device from the cloud. Control appliances through Wi-Fi. Sounds great until the internet suddenly disappears.

Built a ESP32 Interactive voice response system using GeoLinker board and SIM868 GSM module that lets users control devices just by pressing keypad buttons during a call.

No Wi-Fi.
No mobile application.
No internet dependency.
Just pure GSM communication.

The Idea Behind the Project

The concept is actually very simple.

When someone calls the system, it automatically answers the call and plays voice instructions. The user can then press keypad buttons like:

1 to turn ON a device
2 to turn it OFF
(#) to end the call

The system detects these keypad tones using DTMF and performs the assigned action instantly.

It honestly feels like calling a customer care IVR system, except this one control actual hardware.

How Does an IVR System Work?

Most IoT projects students build today completely depend on internet connectivity.

But GSM still has one huge advantage.

It works almost everywhere.

Even in locations where Wi-Fi signals are weak or unavailable, normal cellular networks are usually active. That makes GSM-based systems surprisingly practical for real-world automation.

This project proved that you can still build smart systems without relying entirely on cloud platforms.

Meet the GeoLinker Board

The main controller used here is the GeoLinker GL868 board.

And honestly, it packs a lot into one small board:

ESP32-S3
SIM868 GSM module
GPS support
Wi-Fi + BLE
battery charging
USB-C programming

Having everything integrated made the setup much cleaner compared to using separate GSM and ESP32 modules.

Hardware Setup for the ESP32 Interactive Voice Response System

This project doesn’t just detect calls.

It actually talks back.

The ESP32 stores audio files in LittleFS memory and plays them during the call using sigma-delta modulation. Since raw digital signals cannot directly go into the SIM868 microphone input, an audio filter circuit is used to smooth the signal into proper analog audio.

That part honestly felt more like building a mini telecom system than a regular embedded project.

And hearing the welcome message play through a phone call for the first time was extremely satisfying.

How the Workflow Looks

The entire process feels smooth once the system starts running.

User makes a phone call
SIM868 detects the incoming call
ESP32 answers automatically
Welcome audio plays
User presses keypad buttons
DTMF tones get detected
GPIO outputs switch ON/OFF
Confirmation audio plays back

Everything happens in real time without internet access.

That’s the coolest part.

Problems I Ran Into

Of course, the project wasn’t plug-and-play.

GSM Module Stability

SIM868 modules draw high current during network communication.

Initially, the board kept restarting randomly during calls because the power supply wasn’t stable enough. Using a stronger supply fixed the issue immediately.

Distorted Audio Output

The first audio output sounded terrible.

Turns out the low-pass filter section was extremely important. Without proper filtering, the sigma-delta output carried too much switching noise into the SIM868 microphone input.

After tuning the filter components, the voice playback became much cleaner.

UART Communication Errors

At one point, AT commands stopped responding properly.

Classic RX-TX confusion.

Swapping the UART lines solved it instantly.

Engineering debugging in one sentence.

What I Learned from This Build

This project taught way more than just device control.

I got hands-on experience with:

GSM communication
DTMF detection
UART handling
embedded audio playback
state machines
signal filtering
real-time control systems

And unlike many beginner IoT projects, this one actually felt like a practical product.

Real-World Applications

This type of system can be used for:

home automation
agricultural motor control
industrial switching
remote monitoring
security systems
offline automation setups

Especially in rural areas where internet reliability is poor, GSM-based systems still make a lot of sense.

You’re combining hardware control, telecom concepts, embedded programming, and audio processing into one working system. And the best part is seeing appliances respond to phone keypad inputs in real time.

It genuinely feels like building your own mini telecom automation platform from scratch.

ESP32 Projects