CSSAdet: Real-Time End-to-End Small Object Detection for Power Transmission Line Inspection

Ensuring power transmission line reliability is crucial for power system stability. Mechanical joints, critical components of transmission lines, are susceptible to pin defects, leading to severe consequences. We propose an end-to-end lightweight defect detection method, Cross-Scale Spatial Attention Detector (CSSAdet), for accurately identifying pin defects in mechanical joints. CSSAdet integrates spatial and cross-scale attention mechanisms, enhancing feature representation and improving detection accuracy and recall. Our detection pipeline involves two stages: detecting mechanical joints in UAV-acquired images and identifying pin statuses within detected joints, using a single CSSAdet model. We evaluated CSSAdet on a large dataset of transmission line images, comparing it with existing methods. CSSAdet-2 achieves an average precision (AP) of 77.1% and a recall of 91.4% for joint detection, and an AP of 77.2% and a recall of 92.1% for pin status recognition, with a processing speed of 116.8 FPS. CSSAdet demonstrates superior performance in detecting mechanical joint defects with high efficiency, providing a valuable tool for maintaining and monitoring transmission line integrity and improving power system reliability and safety.