Aiming at the problem that the existing insulator detection algorithm cannot detect insulators and their defects in an oriented manner, an aerial insulator identification and defect detection method improved by YOLOv5 algorithm is proposed. By orienting the aerial insulator pictures, the aerial insulator dataset and defective insulator dataset are formed. The lightweight attention mechanism module is introduced in the backbone feature extraction network of YOLOv5, and the improved spatial pyramid pooling structure is used in the feature fusion stage. By improving the head structure of the YOLOv5 network, the network can perform directional identification of insulators and add angular loss classification to the loss function. The experimental results show that under the premise that the detection time does not increase significantly from 0. 044 s to 0. 049 s per sheet, the value of mAP (mean average precision) on the test set of the improved algorithm is 95. 00%, which realizes directional identification of insulators and their leakage cap defects, and can also be applied to insulator video stream detection. This provides a good basis for the subsequent precise positioning of insulators and further fault detection.