In order to solve the problems of low detection accuracy and poor detection effect caused by the characteristics of multiple defects of transmission line insulators, such as small size and complex background, an improved insulator multi-defect detection algorithm based on YOLO was proposed. Firstly, the FasterNet module was introduced to replace the original module backbone network to reduce the number of model parameters. In terms of feature extraction, the scale-sequence feature fusing module (SSFF) is introduced to combine the high-level information of the deep feature map with the detailed information of the shallow feature map, fully integrate the multi-scale features, and improve the distinction of different defects of the insulator. Finally, a triple feature encoding module (TFE) was introduced to better capture the details of small targets and enhance the recognition of overlapping and small targets. Experimental results show that the average accuracy of the algorithm (mAP0.5) is increased by 3.8% compared with the baseline algorithm, the accuracy of small-target detection such as damage and flashover is increased by 7.7% and 7.9%, and the number of parameters is reduced by 20%, respectively, which improves the identification of different insulator defects while meeting the lightweight requirements. The algorithm has practical guiding significance for the practical application of insulator multi-defect detection.