TLDD-YOLO: An Improved YOLO for Transmission Line Component and Defect Detection

Unmanned Aerial Vehicle (UAV) inspection of transmission lines faces two primary challenges when detecting and analyzing components or defects in videos or images: poor performance in detecting small objects, and interference from complex backgrounds. To enhance defect detection under such cluttered conditions, this paper introduces an improved YOLO-based model, termed Transmission Line Defect Detection–YOLO (TLDD-YOLO), which jointly optimizes feature representation via a Dual-Branch Guided Attention (DBGA) mechanism and a Spatial Offset Attention Module (SOAM). DBGA employs a dual-branch structure to extract high-frequency spatial details and channel-wise semantic information, thereby guiding the backbone network to preserve the critical edge and texture features of small objects, mitigating detail loss during downsampling. SOAM utilizes a lightweight offset generation network to produce spatial offset matrices, and dynamically adjusts feature distributions through offset-guided spatial alignment, enabling feature contours to better conform to object shapes while reducing interference from complex backgrounds. The experimental results on a self-constructed transmission line inspection dataset demonstrate that TLDD-YOLO achieves 57.1% mAP, 83.8% mAP50, and 36.1% mAPs. Compared with the baseline model, the proposed method improves mAP, mAP50, and mAPs by 1.8%, 1.8%, and 7.7%, respectively, confirming its effectiveness for small object detection in UAV-based transmission line inspection.