To address the issues of low edge location accuracy, insufficient multi-scale feature extraction capability, and complex background interference in the detection of small target defects in transmission lines, a target detection algorithm based on STDD-YOLO is proposed. This algorithm enhances the high-frequency feature extraction capability by designing an edge-spatial feature enhancement module, improving the positioning accuracy of defect boundaries and enhancing the perception of high-frequency information such as edges and contours. It replaces the standard convolution in the original Bottleneck with efficient multi-kernel convolution to enhance the network′s detection performance for multi-scale small targets and solve the limitations of the C3k2 structure in utilizing detail information. A shared conv group norm head is designed to suppress background noise interference, enhance the feature expression ability of small targets, effectively improve the robustness of model detection in complex environments, and avoid false detection and missed detection caused by the low saliency of defect targets in complex backgrounds. Experiments show that this algorithm can significantly improve detection accuracy.