In response to the issues of missed and false detections in transmission line defect detection tasks caused by varying target scales, complex backgrounds, and occlusion, where existing object detection algorithms struggle to maintain detection accuracy while ensuring real-time performance, an improved YOLOv10-based UAV transmission line defect detection algorithm, TLDDet, is proposed. First, a faster C2F with attention module(FC2FA) incorporating context anchor attention is designed to enhance feature integration capabilities while reducing the model′s parameters. Then, an attention-based intrascale feature interaction module (AIFI) based on multi-head self-attention is used to improve the model′s detection performance for small objects by enhancing the representation of high-level semantic information in the feature map, thereby increasing the detection accuracy. Finally, an occlusion-aware attention detection head (SEAM-Head) is designed to reduce feature loss caused by occlusion. Experimental results show that the proposed TLDDet reduces the parameters by 33% and the computational cost by 30% compared to the original YOLOv10s model, while improving precision, recall, and mean average precisionfor various transmission line defects by 4.3%, 2.4%, and 3.7%, respectively. The detection speed reaches 143 FPS, and comparative experiments with other real-time detection algorithms demonstrate superior model performance, making TLDDet more suitable for transmission line defect detection tasks.