In order to meet the requirements of high detection accuracy, fast inspection speed and easy to be embedded in mobile devices, a lightweight object detection structure DE-YOLO is designed for mobile terminal devices. Firstly, combining depth-separable convolution, point-by-point convolution and ECA attention mechanism, the feature extraction module NewC3 is proposed, which is responsible for significantly reducing network parameters and strengthening the ability of network to extract effective insulator information. Then, the lightweight module DC-SE is designed with the help of channel number multiplication strategy and channel attention mechanism SE. It is used to weaken the interference of complex background to insulator fault, extract the subtle features of insulator complementary, and then enhance the extraction ability of target feature information of shallow network. Experiments show that the GFLOPs of DE-YOLO network on the expanded Homemade insulator data dataset are reduced by 45%, the running parameters are reduced by 42%, and the detection accuracy of self-exploding defects is up to 93. 2%. NewC3 and DC-SE can ensure the lightweight of DE-YOLO and meet the requirements of real-time detection of self-exploding insulator defects.