Abstract: In order to reduce the inspection burden of electric power workers and realize intelligent inspection in substation, the algorithm of substation equipment defect detection is studied. Firstly, the data augmentation method is used to expand the initial dataset and various image processing method is used to generate the dataset with complex illumination environment. Then, the adaptive spatial feature fusion method is used to mitigate the inconsistency of different scale features in the feature pyramid, and the loss function of confidence is changed to Focal loss function to mitigate the imbalance between positive and negative samples. Based on the improved YOLOX-s(You Only Look Once X-s) network model, the algorithm of substation defect detection is designed. Finally, the detection effect of the improved YOLOX-s model is compared with that of other deep learning algorithms. Under the designed data set, the experiment shows that the comprehensive detection effect of the improved YOLOX-s network model is good, and the accuracy and real-time performance is satisfied. 

Key words: substation, defect detection, data augmentation, you only look once X(YOLOX) net