基于跨分量协同融合与多阶非局部通道注意力的绝缘子缺陷检测方法OA

In allusion to the high similarity between targets and background in insulator images,as well as the defect features of small targets are prone to being diluted due to downsampling and limited receptive fields,an insulator defect detection algorithm based on cross-component collaborative fusion and multi-order non-local channel attention is proposed.A cross-component collaborative fusion module is integrated into the backbone network,and the cross-domain fusion is conducted by means of fre-quency domain and spatial domain feature,to realize the multi-scale c and enhance the feature discrimination ability,thereby im-proving the recognition effect of subtle defect differences.In the neck network,a multi-order non-local channel attention mecha-nism is introduced to capture inter-channel correlations at multiple scales.In combination with non-local perception,it enhances the representation of small defect regions,suppresses feature dilution caused by downsampling,and then improves detection accuracy for small-scale defects.The experimental results show that the improved model can realize mAP@0.5 and mAP@0.5:0.95 of 79.7%and 38.6%,respectively,which are 3.6%and 3.8%higher than those of YOLOv8 benchmark model.The AP in the in-sulator defect category can reach 79.3%,and the frame rate can reach 60.2 f/s,which can meet the real-time detection require-ments of the power system and significantly improve the detection accuracy of insulator defects.