Overhead transmission lines operate in complex environments， where external damage and foreign object intrusion can cause varying degrees of damage to the lines. To address the issues of missed detection and false detection in unmanned aerial vehicle （UAV） inspection， this paper proposes an overhead line hazard identification method based on the improved YOLOv8 （You Only Look Once Version 8， YOLOv8）. A C2f-ODConv （Omni-Dimensional Dynamic Convolution， ODConv） structure is designed to capture richer contextual information， effectively enhancing the feature extraction capability of the model； an SPPF （Spatial Pyramid Pooling Fast， SPPF）-MHSA （Multi-Head Self-Attention， MHSA） module is proposed to improve the detection rate of the model for small occluded targets in complex backgrounds； a parameter-free SimAM （Similarity-based Attention Mechanism， SimAM） attention mechanism is added to the neck network to strengthen the model's focus on important information； an Inner-IoU （Intersection over Union， Inner-IoU） loss function based on auxiliary bounding boxes is introduced， enabling the model to achieve a mean average precision （mAP） of 94%， representing an increase of 3.8%. The effectiveness and superiority of the proposed method are verified through ablation experiments and comparative experiments.