Abstract: To ensure the safe operation of the power system,daily inspection of high voltage insulators using UAV inspection techniques is necessary.However,the influence of power line magnetic field and flight safety leads to a reduction of insulator pixel representation in the image data,which in turn reduces the accuracy of insulator detection.To address these issues,this paper proposes an efficient optimization YOLOX(EO-YOLOX) detection model.Firstly,the model makes use of the idea of atrous convolution and proposes the atrous spatial pyramid pooling(ASPP) module,which eliminates the irrelevant information in the image and improves the ability of the network to identify the region of interest.Secondly,the attentione feature fusion(AFF) module is ad-ded to the feature fusion stage,which improves the accuracy of detecting insulators by supplementing deep semantic and shallow detail information into the fused feature map.Finally,for the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes,this paper proposes an optimised loss function to more accurately assess the quality of the bounding boxes.Experiments and tests are carried out on the insulator dataset,and the experiment results show that the proposed algorithm performs excellently in identifying insulators,with an improvement of about 2.59% in mAP value,compared with the traditional YOLOX method.The real-time processing efficiency of the model is as high as 41.21 frames per second,which effectively solves the insulator detection problem.

Key words: Insulator detection, Atrous convolution, EO-YOLOX, Insulator dataset