High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines

The Unmanned Aerial Vehicle (UAV) inspection mode has been gradually implemented in the power system. The UAV inspection image is checked by the target detection technology, but there is no high-precision target detection algorithm as the technical support. In this regard, this paper proposes a target detection algorithm based on the improved RetinaNet which is suitable for transmission lines defect detection. In this algorithm, the shortcomings of the RetinaNet anchor frame extraction mechanism based on Apriori are corrected. At the same time, the number and size of anchor frames are redesigned by using the improved K-means + + algorithm, so that the anchor frame of the improved algorithm gets the highest average IoU (Intersection over Union) value, which matches the actual size of the transmission line defects. Then, in RetinaNet, the feature pyramid network based on DenseNet is built as the backbone network to improve the model accuracy and make the model lighter. The improved model is trained and tested by using the data set of transmission line defects for validation. The results show that the proposed method has advantages and effectiveness in the detection of transmission line defects, and meets the requirements of intelligent inspection in terms of accuracy.