DAICW: Defect Detection Algorithm for High-voltage Transmission Lines in Complex Weather

High-voltage transmission lines are continuously exposed to outdoor environments, where harsh natural conditions can lead to struc tural damage. Addressing the challenge of defect detection under com plex weather conditions, we propose the Detection Algorithm in Complex Weather DAICW . Firstly, we introduce the Detail-Enhanced Convolu tion DEConv , designed to extract richer features without increasing the parameters. Subsequently, a Focus-Detect is incorporated to emphasize defect features within images while suppressing background interference. Finally, using the LInner-IoU loss function can effectively accelerate con vergence and improve the model’s ability to detect small objects. Exper iments with other mainstream models reveal that DAICW achieves a detection precision of 78.2 and a recall of 67.8 , showcasing robust adaptability in detecting multiple defect types under complex weather scenarios.