Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards

The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and equipment waste is fastest-growing rate industrialized world. One elements printed circuit board (PCB) almost every a PCB inside it. While (WPCB) recycling may result recovery potentially precious materials reuse some components, it challenging task because its composition diversity requires cautious pre-processing stage achieve optimal outcomes. Our research focused proposing method evaluate economic feasibility integrated circuits (ICs) from WPCB. proposed can help decide whether dismantle separate WPCB before physical or mechanical process consists estimating IC area WPCB, calculating IC’s weight using surface density, much metal be recovered by those ICs. To estimate we used state-of-the-art object detection deep learning model (YOLO) DSLR image dataset detect WPCB’s Regarding detection, best was obtained partitioned analysis each through sliding window, thus creating new images smaller dimensions, reaching 86.77% mAP. As final result, that Deep Dataset total 1079.18 g ICs, which would possible recover at least 909.94 metals silicon all WPCBs’ Since there high variability compositions WPCBs, calculate gross income for use as decision criterion type pre-processing.