To address the problems of low accuracy, easy false detection and missed detection in the existing insulator self-explosion defect detection methods under complex backgrounds and foggy environments, an improved YOLOv8 insulator self-explosion defect detection algorithm is proposed. First, the SPD-Conv module for low resolution image and small target detection is introduced into the backbone network to fully extract the feature information of insulator defect target. Secondly, BiFPN is integrated with the SimAM attention mechanism to build the BiFPN_SimAM module, replacing the concat connection of PANet to achieves multi-scale feature fusion and enhances the overall performance of the network. The experimental results show that the precision and mAP@0.5 of the improved algorithm for insulator self-explosion defect detection reach 95% and 93.1%, respectively, which are increased by 1.8% and 1.5% compared with the original YOLOv8 algorithm, and it also has a good detection effect on insulator self-explosion defect detection under complex background and foggy environment.