Abstract: Aiming at the problems of incomplete information representation and easy loss of small-target features in lightning arrester fault detection using traditional single-modality images,this study proposes a lightning arrester fault identification method that integrates the multiscale residual pyramid attention network(MSRPAN) and the lightweight target detection model YOLO11-CGB.MSRPAN extracts multi-scale deep features from multi-modality images and combines the residual attention mechanism to avoid gradient disappearance,enhancing the feature expression ability to address the feature defects of single-modality images.The designed YOLO11-CGB model embeds the convolutional block attention module(CBAM) in the backbone network,uses GhostConv to replace the traditional convolutional layer to reduce the computational complexity,and combines the bidirectional feature pyramid network(BiFPN) to optimize multi-scale feature fusion,improving the small-target detection ability in complex backgrounds.Experiments show that the MSRPAN fusion method is superior to common fusion algorithms such as IHS,Brovey,PCA,WT,and CNN in both subjective and objective evaluations.The YOLO11-CGB model achieves a mean average precision at IoU＝0.5(mAP@0.5) of 94.88% and a recall rate of 94% on the self-built dataset.The recognition confidence levels for the damage(P),flashover(S),and crack(L) faults of the fused images can reach up to 0.88,0.85,and 0.84 respectively,which are better than those of single-modality images(infrared and visible-light images).

Key words: Lightning arrester fault identification, Multi-source image fusion, MSRPAN, YOLO11-CGB, CBAM