A pantograph horn detection method based on deep learning network

A good contact between the pantograph and catenary ensures the safety of high-speed train operation. Pantograph horn, which is the curved structure at both ends of the pantograph, plays important roles in monitoring the operation state of the train. Nowadays, deep learning method has a significant effect in the detection of horns and fault. In this paper, a pantograph horn detection method has been proposed. The method is based on single-shot mutibox detector(SSD) method, which is a real-time method and also with high detection accuracy. A on-orbit image data set with multiple viewing angles and multiple pantograph types is collected to be used in the training stage. The target region is converged through the combination of the feature map in early convolution layers and the prior knowledge. Then, detection results with the partial image and global image as input are obtained, and high accuracy detecting result is generated after confidential decision. Results on actual datasets show that our method can stably obtain accurate horn location, and help to monitor the pantograph status. Moreover, pantograph defects of several common pantograph types can be detected robustly.