Real time electronic-waste classification algorithms using the computer vision based on Convolutional Neural Network (CNN): Enhanced environmental incentives

• Published in: Resources, conservation and recycling Vol. 207; p. 107651
• Main Authors: Sarswat, Prashant K., Singh, Rahulkumar Sunil, Pathapati, Subbu Venkata Satyasri Harsha
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: aluminum; Computer vision; Convolutional neural network; copper; data collection; Deep learning; E-waste; economic valuation; electronic wastes; glass; neural networks; Object detection; prediction; Sorting; steel; TensorFlow; YOLO; Deep learning; E-waste; YOLO; Computer vision; Convolutional neural network; Object detection; Sorting; TensorFlow
• ISSN: 0921-3449; 1879-0658
• DOI: 10.1016/j.resconrec.2024.107651

•The deep learning models were utilized to detect different e-waste components.•Both TensorFlow and PyTorch modules were utilized for object detection.•An independent evaluation of 240 e-waste objects was performed using YOLO 7 model, which show ∼94 % correct prediction for batch size of 16. An innovative approach is needed to boost the economic value of e-waste by improving metal recovery and facilitating the separation of plastics and valuable metal components. Leveraging deep learning and computer vision technology offers a promising solution for automatically categorizing and sorting e-waste components like copper, printed circuit boards (PCB), steel, glass, and aluminum, presenting significant financial and environmental incentives for increased recycling efforts. In this instance, the real-time object identification algorithms YOLO 7 and 5 were used along with TensorFlow version 2.8.0. The e-waste dataset works incredibly well with TensorFlow and YOLO. For the detection of copper, PCBs, and plastic, the F1 values (F1 score is the harmonic mean of precision and recall; precision is the fraction of relevant instances among the retrieved instances whereas recall or sensitivity is the fraction of relevant instances that were retrieved) were as good as 1.0, whereas the score for steel and aluminum was 0.8. The mean average precision (mAP; for a set of queries, it is the mean of the average precision scores for each query) was 0.96 for all classes, with the highest precision for copper (0.99) followed by PCB (0.981). 240 e-waste objects were independently evaluated using the YOLO v7 model, achieving a remarkable ∼94 % prediction accuracy with a batch size of 16, ensuring robust performance. The real-time e-waste component detection was also done using video clips as well as webcam streaming. Deploying real-time e-waste object detection and sorting can significantly narrow the gap between e-waste accumulation and recycling rates, leading to reduced environmental strain and impact. [Display omitted]