The latest articles on DEV Community by Dhananjayan P N (@dhananjayanpn). https://dev.to/dhananjayanpn https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F390240%2F36c0a933-59ac-4712-87d4-92cb7c457d23.jpeg https://dev.to/dhananjayanpn en Dhananjayan P N Wed, 20 May 2020 07:43:27 +0000 https://dev.to/dhananjayanpn/ai-waste-segregation-system-ph2 https://dev.to/dhananjayanpn/ai-waste-segregation-system-ph2 <h2> My Final Project </h2> <p>An AI Waste Segregation System that uses Machine Learning and Object Detection to detect the type of waste and segregate it into Biodegradable and Non-Biodegradable depending on the results of the Object Detection. It is a stand-alone system that can be used in a multitude of locations.</p> <h2> Link to Code </h2> <div class="ltag-github-readme-tag"> <div class="readme-overview"> <h2> <img src="https://res.cloudinary.com/practicaldev/image/fetch/s--vWogaON8--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://practicaldev-herokuapp-com.freetls.fastly.net/assets/github-logo-28d89282e0daa1e2496205e2f218a44c755b0dd6536bbadf5ed5a44a7ca54716.svg" alt="GitHub logo"> <a href="https://github.com/Dhananjayan-PN"> Dhananjayan-PN </a> / <a href="https://github.com/Dhananjayan-PN/AutoDust"> AutoDust </a> </h2> <h3> AI Waste Segregation System to clean the world one AutoDust at a time </h3> </div> <div class="ltag-github-body"> <div id="readme" class="md"> <h1> AutoDust</h1> <p>An AI Waste Segregation System that uses Machine Learning and Object Detection to detect the type of waste and segregate it into Biodegradable and Non-Biodegradable depending on the results of the Object Detection. It is a stand-alone system which can be used in a multitude of locations.</p> </div> </div> <br> <div class="gh-btn-container"><a class="gh-btn" href="https://github.com/Dhananjayan-PN/AutoDust">View on GitHub</a></div> <br> </div> <br> <h2> How we built it </h2> <p>The system was built entirely in Python. For the Object Detection, we used YOLOv3 as our trained model to accurately detect the type of waste. We used a RaspberryPi to control the motors depending on the command from the server. Yes, we divided the workload into 2, the server (cloud) and the client (RaspberryPi), which made it much faster and efficient.</p> <h2> Additional Thoughts </h2> <p>We learned a lot of new things in this project including how to use the Cloud (thanks AWS) to your benefit. We also learned how we could efficiently divide the workload for better performance. All-in-all great experience!</p> octograd2020 githubsdp machinelearning python