A sensor combination based automatic sorting system for waste washing machine parts

In order to solve the difficulties encountered in E-wastes disposal automation, this work investigates the sensor-based sorting of waste washing machine parts. To satisfy the post-processing, we divided the parts into 6 categories (Piece, Cover, Base, Shell, Spin tub and Drain hose) based on the shape differences and used object recognition algorithm to classify them. In the recycling terminal, parts were divided based on types of materials. Piece and Cover contained acrylonitrile butadiene styrene (ABS) parts and polystyrene (PS) parts, while Base, Shell, Spin tub and Drain hose were polypropylene (PP) parts only. Therefore near-infrared (NIR) spectroscopy was applied for sorting ABS, PS and PP. Algorithm for object recognition and NIR spectroscopy both reached high precision. In object recognition, algorithm of YOLOv5 and its improved models, including focal loss and channel attention mechanism, were tested. YOLOv5 with channel attention achieved the best 98.7% mean Average Precision (mAP) and the overall over 98% Average Precision (AP). In the task of NIR spectroscopy, principal component analysis (PCA) coupled with support vector machine (SVM) reached the overall 97.8% classification precision. Finally, a sorting platform based on our device and algorithm was designed.