Image Segmentation and Classification of E-waste for Training Robots for Waste Segregation

Abstract--Industry partners provided a problem statement that involves classifying electronic waste using machine learning models, which will be utilized by pick-and-place robots for waste segregation. This was achieved by taking common electronic waste items, such as a mouse and a charger, unsol-dering them, and taking pictures to create a custom dataset. The state-of-the-art YOLOv11 model was trained and run to achieve 70 mAP in real-time. The Mask R-CNN model was also trained and achieved 41 mAP . The model can be integrated with pick-and-place robots to perform segregation of e-waste. Electronic waste (e-waste) is one of the fastest-growing solid waste streams globally [2].