Timely detection of Self-exploding insulators during the inspection process can effectively prevent power grid accidents. In response to the problems of large memory and slow detection speed required for convolutional neural network training, which do not have advantages in real-time detection on mobile devices, self-explosion fault detection algorithm for Insulator based on improved YOLOv7-tiny is proposed. Firstly, deformable convolution and dynamic snake convolution is introduced into the YOLOv7 tinyIncorporating dynamic serpentine convolution into the YOLOv7-tiny algorithm and designing a more efficient layer network to enhance perception; then, the Gold-YOLO network is introduced to enhance the information fusion of the intermediate layer; subsequently, the MPDIoU loss function is used to reduce the redundancy of the prediction boundary; finally, designing a Self-exploding insulator detection system to enable staff to quickly identify self-exploding insulators in a massive collection of images. The research results show that the mean average precision of the improved algorithm is 96.3%, which is 1.1% higher than the original YOLOv7-tiny algorithm. The average precision of the improved algorithm is 99.5% for identifying Self-exploding insulators, which is 0.2% higher than the YOLOv7-tiny algorithm and 0.1% higher than the YOLOv7 algorithm. Moreover, the scale of the improved algorithm is only 28% of that of the YOLOv7 algorithm, and the FPS has increased by 11.3, reaching 60.6. The improved algorithm can meet the requirements of real-time detection while ensuring recognition accuracy.