Research on Metal Foreign Object Detection Algorithm Based on RECBAM-YOLOv5

Abstract High-voltage switchgear plays a crucial role in power systems, and its reliability is vital for the safety and stability of the grid. One major issue affecting high-voltage switchgear operation is the occurrence of discharge phenomena, which can damage the equipment and reduce grid efficiency. A common cause of discharge is metallic foreign objects inside the equipment. Detecting and removing these objects is key to preventing such discharges. This paper proposes a detection algorithm based on YOLOv5, enhanced by the integration of a Residual Efficient Convolutional Block Attention Module (RECBAM). The introduction of RECBAM into the YOLOv5 architecture improves the network’s feature extraction performance and the accuracy of detecting metallic foreign objects. Experimental results demonstrate that the proposed method achieves an average detection accuracy of 95.20%, which is 1.5% higher than the original YOLOv5, and the recall rate improves by 1.2%. Visualization results further show the superior performance of the proposed approach in metallic foreign object detection tasks.