Abstract: To balance the accuracy and complexity of transmission line insulator defect detection algorithms, a high-precision lightweight detection algorithm based on YOLOv8n is proposed. first, the C2f_MSCA module is constructed by embedding multi-scale coordinate attention(MSCA) into the csp bottleneck with 2 convolutions(C2f) module, replacing it in the backbone network to enhance feature extraction capabilities. Then, light symptotic feature pyramid network(LightAfPN) is introduced and optimized with depthwise separable convolution(DSConv) and CA for lightweight operation and improved attention to critical features. The neck network integrates the small target feature layer P2 while removing the redundant high-level feature P5 from the backbone, further reducing model complexity and enhancing small target extraction. The space to depth convolution(SPDConv) is introduced to compensate for the accuracy loss resulting from the removal of the P5 layer. finally, the differentiated component-wise modulated scylla-ioU(DCM-SioU) is designed to replace YOLOv8n's complete intersection over union(CioU), dynamically adjusting the penalty strategy during different training phases to optimize confidence loss. Experimental results show that the recognition model has achieved an average accuracy of 96.04% for the intersection to union ratio threshold of 0.5 for the insulator body and three different insulator defects. Compared with the benchmark model YOLOv8n, it improves by 3.59 percentage points and reduces the number of parameters by 29.5%, achieving a balance between model accuracy and complexity. This can provide reference for deploying object detection models at the edge.

Key words: YOLOv8n, insulator defects, small goals, LightAfPN, SioU, edge end