Due to the frequent occurrence of foreign objects hanging on equipment in substations,in order to prevent power accidents caused by untimely inspection by station personnel,a research on substation foreign object detection integrating attention mechanism and Bi-YOLO is proposed based on YOLOv5.The weighted bidirectional feature pyramid is used to replace the original feature pyramid network,the coordinate attention module is embedded in C3 structure,and the mixed attention module is added to improve the feature extraction ability and detection efficiency of foreign bodies in substation.The test results show that compared with the YOLOv5 algorithm,the multi-class average detection accuracy of the proposed detection algorithm is increased by 3.3%,and the mAP value is up to 91.3%,meeting the real-time requirements.
维普
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