In order to realize the accurate positioning and detection effect of insulators during circuit inspection, this paper proposes an improved YOLOv5 insulator detection model based on Dense-Block and rotating frame. Aiming at the characteristics of large length width ratio and changeable direction of insulator, this model proposes to use the long side definition method to add angle information to the detection frame, realize the target rotation frame detection, and effectively improve the effect of insulator detection and positioning. At the same time, in order to enhance the reuse and propagation of features, this paper uses dense block to improve the residual module in the model and build YOLOv5-dense detection model. Finally, in order to enable the YOLOv5-dense model to pay more attention to effective feature information, a SimAM attention module is added at the end of the backbone network to improve the model. Before the experiment, Retinex algorithm is used to enhance the input insulator image. The experimental results show that compared to the original YOLOv5 algorithm, the algorithm proposed has improved average accuracy and processing frames per second. In addition, compared with the horizontal frame detection algorithm, this algorithm removes a large amount of redundant background information in the detection results, and realizes more accurate positioning of the insulator area.