Insulator is one of the important components on the transmission line. It is an important means to ensure the safe transmission of power to accurately detect the insulator and its defects using UAV patrol inspection. In order to solve the problem that the main target detection network directly scales the original image when processing high-resolution images, which leads to the loss of target details or re detects the original image by cutting it into blocks, which leads to the loss of the overall information of the target, a dual branch structure backbone network (RC Net) is designed based on the residual network (ResNet 50), which can reduce the loss of target context information and local information. At the same time, the deformable convolution is introduced to replace part of the conventional convolution to change the sampling points, so that the sampling points can more closely fit the geometric shape of the target itself, improve the feature expression ability of the network, and redesign the parameters of the anchor frame according to the size and shape of the insulator itself, so that the anchor frame is more suitable for the scale of the target itself, and the frame regression is more accurate. The experimental results on the expanded Chinese transmission line insulator dataset (CPLID) show that the average accuracy of the algorithm proposed in this paper reaches 88.3%, which is better than the current mainstream detection algorithm in the high-resolution image background.