Abstract: Traditional defect detection for overhead transmission line insulator is generally carried out by manual inspection. The increase in the number of overhead transmission lines has made larger scale and more complex environment of the inspection, which amplifies the shortcomings of traditional insulator defect detection methods with high labor costs and low detection efficiency. New line inspection methods such as unmanned aerial vehicle (UAV) rely on deep learning object detection algorithms to identify insulator defects in overhead transmission lines, which effectively deals with shortcomings of manual inspection and becomes the development trend of insulator defect detection. Therefore, focusing on defect detection scenario of overhead transmission line insulator, we firstly sorted out the commonly used deep learning object detection algorithms, and compared the detection strategies, detection accuracy and detection speed of different algorithms. Then, combined with the cloud-edge-end collaborative architecture, the improvement requirements of the algorithms and corresponding improvement methods of the algorithms were explained. Finally, in response to the shortcomings of existing insulator detection, the identification of multiple types of defects in transmission line insulators is prospected, and under this research trend, the value of model edge lightweight and algorithm research for small sample data is further explored.