DEV Community: William George

Image Processing Projects for Final Year Students

William George — Mon, 05 May 2025 05:52:29 +0000

Hello Developer Community! Welcome to my blog featuring ten engaging image-processing projects ideal for final-year students. These hands-on tutorials cover OpenCV, machine learning, IoT integration, ESP32, Arduino, TensorFlow and more including applications like license plate recognition, gesture control and facial-attendance systems. Dive in to enhance your skills and practical understanding today.

Number Plate Recognition System

Capturing a moving car’s ID number is challenging for the human eye, but this project makes it possible. The number plate detection system using the ESP32-CAM, programmed via the Arduino IDE, captures an image of a vehicle’s number plate and sends it to a cloud server through an HTTP POST request over Wi-Fi. The server performs OCR to extract the plate number and returns the result in JSON format. The ESP32-CAM then parses this response and displays it on an OLED screen. The process is triggered by a button press. As all recognition is handled in the cloud, the device code remains simple. This system can be used in smart parking, toll collection and access control. By trying this project, students learn about embedded imaging, networking and API integration.

Face Recognition Attendance System

Here is an automated Face recognition attendance system project built using OpenCV and machine learning. It employs the LBPH algorithm to detect and identify faces through a camera and stores data in a MySQL database. The system captures user images, trains models and updates attendance records in real-time. A Tkinter-based GUI facilitates user interaction, while a web interface enables attendance checks via a web dashboard. Suitable for classrooms or workplaces, it streamlines attendance tracking by eliminating manual processes. By this project, students gain hands-on experience in ML algorithms, OpenCV workflows, database integration, GUI development and full-stack web technologies, strengthening their skills in real-world AI applications and system design.

Gesture Controlled Video Player

In this Gesture Controlled Video Player using Raspberry Pi and MediaPipe project, hand gestures are detected via a Pi camera, mapped to media controls using PyAutoGUI for keyboard automation. MediaPipe’s hand-tracking model processes real-time video, while OpenCV handles image analysis. Ideal for smart home systems, presentations or accessibility tools, it demonstrates hands-free interaction. Students learn real-time computer vision, ML model integration (MediaPipe), Python scripting and hardware-software interfacing.

Driver Drowsiness Detection System

There is always a concern about driving at nighttime as we might fall asleep unknowingly, which may result in accidents. To prevent such incidents, here is a Driver Drowsiness Detection System using Python, OpenCV and Keras to detect driver drowsiness and prevent accidents. A webcam captures real-time video, where Haar cascade classifiers identify faces and eyes. A Convolutional Neural Network (CNN) model which is trained on 7000 custom-collected eye images and classifies eye states. The system tracks eye closure duration using a scoring mechanism. A prolonged closure triggers an audible alarm. Suitable for automotive safety, it demonstrates real-time computer vision and ML integration. Students learn CNN architecture design, Haar cascade object detection, image preprocessing and real-time system implementation, applying AI to solve critical safety challenges from this project.

Emotion Recognition System

Think about a DIY project where you can identify emotions of the individuals. This Emotion Recognition System project uses Raspberry Pi 4, OpenCV, TensorFlow and Keras to detect facial expressions (Angry, Fear, Happy, Neutral, Sad, Surprise) in real-time. A pre-trained CNN model (trained on the FER2013 dataset) classifies emotions using Haar cascades for face detection and ROI extraction. The Pi Camera captures video, processes frames with OpenCV and displays predictions on-screen. Suitable for sentiment analysis, mental health monitoring or interactive gaming, it demonstrates real-time computer vision and ML integration. Students learn CNN deployment, Haar cascade workflows, image preprocessing and Raspberry Pi hardware-software interfacing.

Lane Detection System

Can you believe that you can build a system to prevent your vehicle from colliding with the roadside? Yes, with this Lane Detection System project, you can make it possible. This project builds an ADAS using NVIDIA Jetson or Raspberry Pi, LIDAR and OpenCV to detect road lanes and obstacles. A camera captures real-time video, processed via edge detection algorithms to identify lanes, while LIDAR maps surroundings for collision avoidance. The system displays processed data on an HDMI screen, combining lane tracking and anti-collision alerts. Suitable for automotive safety, it demonstrates real-time sensor fusion (camera + LIDAR) and embedded computing. Students learn OpenCV image processing, LIDAR integration, Python scripting and multi-threaded system design, applying ADAS principles to enhance vehicular safety through hardware-software co-development.

AI Threat Detection System

If you wish to build a security system that protects your place, this AI-driven threat detection system project, which uses Edge AI with Sipeed Maixduino and NodeMCU to detect weapons (rifles, handguns) in real-time is an ideal solution. A Yolov2 model that is trained on 2083 images will process camera input locally. LoRa SX1278 transmits alerts to mobile units within 8-10 km, while Wi-Fi sends images to a Node-RED dashboard for centralized monitoring. Designed for conflict zones, it enables rapid police alerts if it finds threats. Students learn Edge AI deployment, LoRa/Wi-Fi integration, model training (TensorFlow Lite) and real-time embedded systems, bridging hardware-software workflows for security applications.

Fruit Sorting System

If you want to build a system where you can learn electronics and also create a tool that helps sort items manually (which may take significant time and concentration), this automatic sorting system using Raspberry Pi automates fruit sorting with the help of OpenCV and TensorFlow. A camera captures images of fruits (e.g., oranges) processed via a TensorFlow model trained to detect specific fruit classes. Identified fruits trigger a servo motor to sort them into designated baskets, while a counter logs totals with timestamps. Designed for farms or food processing units, it replaces manual sorting and improving efficiency. Students learn computer vision (OpenCV), machine learning model integration (TensorFlow), servo motor control and Python scripting, gaining hands-on experience in AI-driven automation and IoT system design for agricultural applications or other applications that need sorting.

Face Recognition Door Lock System

If you want to enhance your home security beyond traditional methods like the normal lock-and-key system, consider this Face Recognition Door Lock System using ESP32-CAM, a Relay Module and a Solenoid Lock to create a secure, keyless entry system. The ESP32-CAM captures live video, detects faces via built-in libraries and triggers the relay to unlock the door if a recognized face matches stored data. A solenoid lock secures or unlocks the door based on relay signals. Designed for homes or offices, it demonstrates IoT-based security. Students learn ESP32-CAM programming, face detection and recognition workflows, relay control and Arduino IDE integration, gaining practical skills in embedded systems, real-time data processing and hardware-software interfacing for smart security solutions.

Object Detection System

Here is a tutorial about an Object Detection System using OpenCV and Python to automate object detection and recognition. Techniques like SIFT, SURF, FAST, BRIEF and ORB algorithms identify key features (edges, corners) in images, enabling tasks like template matching and corner detection. Applications include robotics, autonomous vehicles and security systems. Students learn computer vision fundamentals, feature extraction and algorithm implementation (Harris corner detection, template matching). They gain hands-on experience in image preprocessing, keypoint detection and real-time object tracking, building skills for scalable solutions in automation, surveillance or augmented reality.

10 Open-Source Projects Using Raspberry Pi and Python

William George — Fri, 28 Mar 2025 12:56:34 +0000

10 Opensource Projects Using Raspberry Pi and Python

Welcome to a collection of Raspberry Pi projects that explore various applications in automation, security and interactive technology. This selection includes projects such as emotion recognition, gesture-controlled media players, voice activated speakers and autonomous robots like line followers and surveillance systems. Also featured are innovative solutions for biometric attendance, license plate recognition, anti-theft flooring and transparent displays using creative design techniques.

Each project highlights the versatility of Raspberry Pi and demonstrates how simple hardware integrations can solve real-world challenges through practical, hands-on approaches. Let’s Explore these projects to discover practical methods and innovative ideas for integrating technology into work.

Emotion Recognizer

This Raspberry Pi Based Emotion Recognition using OpenCV, TensorFlow and Keras project uses a VGG-like CNN trained on the FER2013 dataset to detect faces and classify six emotions - Angry, Fear, Happy, Neutral, Sad and Surprise. The system processes real-time video feeds, extracting facial regions and predicting expressions accurately. Applications include monitoring employee engagement, evaluating patient mental health in medical settings and assisting law enforcement in suspect emotion analysis during interrogations. By integrating robust image processing with compact hardware, this project facilitates advanced emotional analytics for diverse, practical scenarios.

Surveillance Robot

This Raspberry Pi based Android Controlled Surveillance Robot project uses a Raspberry Pi interfaced with a Bluetooth module to control a mobile robot via an Android app. A 360-degree night vision camera mounted on the robot provides live video streaming to the Android device with audio capture and video recording capabilities. The system supports full 360-degree surveillance by rotating the camera, enabling comprehensive monitoring. Designed for challenging security missions, this robot delivers reliable autonomous surveillance in areas where direct human involvement may be unsafe.

Gesture Controlled Video Player

This Gesture Controlled Video Player using Raspberry Pi and MediaPipe project creates a gesture-controlled media player. It detects six distinct hand gestures—open/closed fist to play or pause, upward/downward motions to adjust volume, and left/right swipes to fast-forward or rewind videos. The system translates natural hand movements into intuitive media commands, eliminating the need for traditional remote controls. A practical application is in smart home environments where users can easily control entertainment systems hands-free during activities like cooking or exercise, enhancing convenience and usability while maintaining a modern, touchless interface. This solution exemplifies the future of interactive home technology.

Anti-theft Flooring System

This IOT based Anti-theft Flooring System using Raspberry Pi project uses Raspberry Pi to power an IoT based anti-theft flooring system that detects any movement on secure floor tiles. It employs a piezo sensor to sense steps and a camera to capture images. The Raspberry Pi processes sensor signals and automatically directs the camera to the area of intrusion. The system sends alerts and images over the Internet using an IoT web-based interface. In practical application it provides real time security monitoring for residences. It offers enhanced safety by alerting homeowners instantly in case of unauthorized entry thereby preventing potential theft and ensuring peace of mind.

Voice Controlled Speaker

This How to Build an Amazon Alexa Speaker using Raspberry Pi project creates a smart voice-controlled speaker. A USB microphone captures your voice while a 3.5 mm line in speaker delivers audio responses. The process involves setting up an Amazon Developer Account and registering the device. The device responds to vocal commands to play music, deliver news updates and manage calendars. This project connects advanced voice recognition technology with compact hardware in a simple build that offers an engaging interactive experience in your everyday life. It combines technical innovation with seamless integration into daily routines with ease.

Line Follower Robot

Leveraging the Raspberry Pi Pico with an L298N Motor Driver and two IR sensors, this Raspberry Pi Pico based Line Follower Robot project builds an autonomous line follower car that tracks paths by detecting infrared light reflections. The system interprets sensor data to guide its movement by turning left, right or moving straight based on light absorption differences. The technical design relies on energy efficiency and fast processing using Thonny IDE and MicroPython for programming. In a practical setting, this robot can transport materials in factories or warehouses, navigating marked routes independently. Its compact design and low energy use make it an ideal solution for automated transport.

Camera Based Tanker

This Raspberry Pi Cam Tank V1.0 project builds a cam tank that streams live video and provides remote control capabilities. A Raspberry Pi version B, powered USB hub, USB webcam and WiFi dongle form the system’s core, while high-torque servos drive movement and tower functions. The design incorporates suspension for smoother motion and integrates a tilting BB gun mechanism for added realism. Operating via a web interface, users can control the tank from a smartphone over WiFi. A practical example is remote exploration, where users safely inspect challenging or confined spaces without direct exposure, enhancing operational efficiency.

Glass Dome Based Transparent Display

This project of using a Raspberry Pi with a small round LCD and custom glass dome to create a transparent display using the Pepper's Ghost effect. Inside the dome, a clear reflector is positioned at a 45-degree angle to produce an optical illusion that makes images appear to float. The Raspberry Pi outputs visuals to the LCD housed in the 3D printed base, while a privacy shield conceals the source screen. Open-source code and 3D models are provided for replication. This design can be integrated into interactive digital signage or immersive museum exhibits to captivate audiences with futuristic displays.

Vehicle License Plate Recognizer

This License Plate Recognition Using OpenCV with Raspberry Pi 5 project implements a real-time License Plate Recognition System using Raspberry Pi 5, OpenCV and Tesseract OCR. The system captures live video from a connected camera and detects vehicle license plates with image processing techniques. It then applies Optical Character Recognition to extract the plate numbers, comparing them against a predefined list to classify entries as either Staff or Outsider. Recognized plates are stored with status labels for further processing and documentation. This technical solution automates vehicle identification and is ideal for managing parking facilities and enhancing office security by streamlining access control and record keeping for enhanced improved safety.

Biometric Attendance System with Temperature Recorder and Sanitizer Dispenser

This Raspberry Pi based Biometric Attendance System with Temperature Recorder and Sanitizer Dispenser project uses a combination of Raspberry Pi 4 and Arduino UNO. It integrates HC-SR04 ultrasonic sensors to trigger a 5MP camera and MLX90614 contactless infrared temperature sensor, while an MG995 servo motor activates sanitizer dispensing. Face recognition is handled by a dedicated Python module and a 20x4 LCD with an I2C interface displays details. A 5V active buzzer provides alerts and collected data is sent via email as an Excel file. By uniting sensor data, real-time processing and serial communication, the system automates attendance and body temperature screening seamlessly in work environments, educational institution and other places.