Abstract: The key components in overhead lines may suffer from damage， detachment， and other defects due to long-term exposure to the natural environment. It is difficult to detect and repair these defects manually. To address the aforementioned issues， this paper proposes an light-weighted， edge computing device suited， improved YOLOv5 based detection method. Firstly， an EMA module is added at the end of the backbone network to enhance the network’s ability to capture features. Secondly， the CBS module of the neck will be replaced with GhostConv， and the C3 module of the neck will be combined with SENetV2 to make the network more lightweight while enhancing its representational ability. The experimental results demonstrate that the proposed method achieves a significant improvement in class-average accuracy compared to YOLOv5, while maintaining real-time detection capability with only marginal frame rate reduction. Compared with SSD and Faster R-CNN algorithms， it has certain advantages in detection accuracy and speed.

Key words:  , YOLOv5； object detection； aerial lines； EMA； GhostConv； SENetV2