A computer vision-based system for real-time component identification from waste printed circuit boards

With an exponential increase in consumers' need for electronic products, the world is facing an ever-increasing economic and environmental threat of electronic waste (e-waste). To minimize their adverse effects, e-waste recycling is one of the pivotal factors that can help in minimizing the environmental pollution andto increase recovery of valuable materials. For instance, Printed Circuit Boards (PCBs), while they have several valuable elements, they are hazardous too; and therefore, they form a large chunk of e-waste being generated today. Thus, in recycling PCBs, Electronic Components (ECs) are segregated at first, and separately processed for recovering key elements that could be re-used. However, in the current recycling process, especially in developing nations, humans manually screen ECs, which goes on to affect their health. It also causes losses of valuable materials. Therefore, automated solutions need to be adopted for both to classify and to segregate ECs from waste PCBs. The study proposes a robust EC identification system based on computer vision and deep learning algorithms (YOLOv3) to automate sorting process which would help in further processing. The study uses a publicly available dataset, and a PCB dataset which reflect challenging recycling environments like lighting conditions, cast shadows, orientations, viewpoints, and different cameras/resolutions. The outcome of YOLOv3 detection model based on training of both datasets presents satisfactory classification accuracy and capability of real-time competent identification, which in turn, could help in automatically segregating ECs, while leading towards effective e-waste recycling.