Abstract: The icing of transmission lines can greatly impact the safety and stability of the power grid system. Due to the distribution of transmission lines in mountainous areas， forest areas， and unmanned open areas， workers cannot obtain on-site information in the event of damage such as rain， snow， and freezing. To accurately identify the icing situation of transmission lines in complex environments such as mountainous and uninhabited areas， this paper proposes an improved YOLOv8s-based detection method. Firstly， SIoU is adopted as the loss function to improve the training speed and accuracy of the model.  Secondly， by replacing some ordinary convolutions with dual convolutions， the information exchange between different channels is enhanced， effectively improving the efficiency of feature extraction， thereby further accelerating the convergence speed of the model. Finally， the GiraffeDet network structure is introduced to replace the original network structure and utilizes multi-scale information and the global context of feature map to make the model perform better in detecting small targets and complex scenes， improving the accuracy and robustness of detection. The experimental results show that compared with YOLOv8s， the improved method meets certain requirements for accuracy， reduces the model size by 7.3 MB， and significantly improves speed.

Key words:  , deep learning, power systems, YOLOv8s, transmission line icing detection