Abstract: Catenary is a transmission line over the railway to supply power for electric locomotives,its support and dropper both are the key components of railway power transmission,it will make a huge impact if there is a failure.Catenary accidents may occur in serious cases,could cause hidden danger of High-speed trains.It is of great significance to find an efficient and accurate posi-tioning method of these two equipments to facilitate the subsequent abnormal judgment.This paper focuses on this problem,pre-sents an intelligent data augment algorithm,it can randomly select one or more data augment methods to enhance the catenary picture.In addition,this paper proposes an improved YOLOv3 algorithm,5 groups of feature pyramids with different scales are designed by enhancing feature extraction network.Finally,combining the improved algorithm with the data augment algorithm,to realize dropper and support detection task.The mAP of the algorithm on the test dataset is 93.5%,the recognition rate is 45 fps.This method realizes the real-time detection of dropper and support under the high precision.

Key words: Catenary, Convolutional neural network, Data augmentation, Dropper and support detection, YOLOv3