| Automated Electronic Components Waste Detection Using YOLOv12 |
| Paper ID : 1040-ICEEM2025 (R1) |
| Authors |
|
Ali Mohammed Elhenidy *1, Muhammed E Abd Alkhalec Tharwat2, Saif M. Alsabti3, Raid gaib4 11Computer Engineering and Control Systems Department. Faculty of Engineering, Mansoura University, Egypt.
2 Faculty of Engineering mansoura national university, Gamasa, Egypt. 2Technical College of Engineering, Al-Bayan University, Baghdad, Iraq 3Technical College of Engineering, Al-Bayan University, 4Technical College of Engineering, Al-Bayan University |
| Abstract |
| Abstract—Rapid technical advancements and the growth of the consumer electronics sector have led to an exponential increase in electronic trash, which has become a significant global issue. Because products have shorter lifespans, recycling e-waste, including printed circuit boards, is very important. The recycling process will unavoidably be made more difficult by the prevalence of hazardous elements, high-value materials, and numerous electronic components found in wasted printed circuit boards (PCBs). Because of the high degree of occlusion and intricate overlapping interactions between electronic components, traditional detection methods are typically inefficient in separating and identifying the components in the context of recycling electronic trash. This frequently leads to missed detection and misdetection, which drastically lowers the overall accuracy and dependability of detection. In this paper, an automated AI-based methodology is adopted to detect and localize the electronic waste components. The proposed methodology is built upon the recent YOLOv12 object detection model. YOLOv12 achieves higher mAP and real-time inference response than the previous YOLO versions. |
| Keywords |
| YOLOv12; electronic Waste detection ; Sustainability |
| Status: Accepted |