Hybrid-YOLOv5 for object detection of non-ferrous metals in end-of-life vehicles

This is the authors' abstract. We don't add key points for this paper.

In recent years, the detection of non-ferrous metals in end-of-life vehicles (ELVs) has become essential for improving recycling in the circular economy. Traditional methods struggle with accurate detection due to the variety of metals and challenging industrial environments. This study proposes a Hybrid-YOLOv5-based algorithm for efficiently detecting copper, aluminum, and stainless steel in ELVs. The goal is to enhance detection accuracy and computational efficiency in metal sorting. By integrating the Coarse-to-Fine (C2F) module, Squeeze-and-Excitation (SE) module, and MobileNetV3 backbone, we significantly improve performance and speed. On a dataset of 2,500 infrared images, Hybrid-YOLOv5 achieves 84.2% mAP@0.5 and 60 FPS inference speed, outperforming YOLOv3, YOLOv5, YOLOv7, and YOLOv11 by 22.2%, 12.4%, 11.1%, and 36.2% in mAP@0.5, respectively. This work provides an efficient solution for industrial metal sorting and intelligent recycling in the circular economy.