Leveraging Convolutional Neural Networks (CNNs) for Real-Time Object Detection and Classification in IoT-Enabled Smart Bins for Efficient E-Waste Collection and Sorting

The rapid growth of electronic waste (e-waste) poses significant environmental and logistical challenges, as discarded electronics contain valuable resources and hazardous materials. Effective management and recycling of e-waste are essential to mitigate environmental impact and recover valuable materials. This is a very important factor in the light of the fact that the generation of e-waste is currently on the rise. To the best of our knowledge, this paper puts forward a new technique for efficient Real-time Object Detection and Classification in Smart IoT bins using CNNs which is implemented using python and accuracy is 98%. The proposed system incorporates the most recent CNN algorithms in order to tag and sort e-waste items as they are placed in the smart bins. The building enable the use of modern CNN models like Faster R-CNN, YOLO and SSD for real-time image analysis on the bin’s camera system. These models are subsequently refined using a large dataset of e-waste images to increase the models’ capabilities of identifying different electronic parts and gadgets. It also comprising a light-weight edge compute architecture so that the object detection, as well as its classification, can happen at a very fast speed thereby eliminating the delays inherent in feedback. Using IoT technology in the smart bins allows the bins to connect to the waste management system to relay details of the type and amount of e-waste being dumped. It helps in decisions to be made as to which direction to take the collection trucks, as well as sorting procedures to be followed at recycling facilities.