Automatic and accurate location of insulator components in catenary images is the basis of detecting insulator fault. The insulators in the catenary images are incline-angled, so it is difficult to locate the object accurately by using horizontal box. To solve this problem, an insulator accurate location approach was proposed based on improved RetinaNet. To begin with, the efficient Ghost module was adopted to replace the convolution operation in the original feature extraction network to obtain multi-scale feature maps and reduce the computational burden of the model. Next, in order to suppress the influence of secondary features on object detection, the attention mechanism was embedded in the network. Then, the rotating box was introduced as the prediction box of the model to realize the accurate location of insulators and reduce the interference of redundant background noise. Finally, the positive and negative samples were redefined in the training process. By doing so, the problem of learning wrong samples that caused by adding rotating box was resolved. Experimental results demonstrate that the proposed approach featuring good detection performance can locate the insulator accurately and prevent redundant background information. Compared with original algorithm, the detection accuracy increases by 2. 8%, the detection speed reaches to 25. 6 FPS, and the number of network parameters reduces by 42. 8%.