Fault Diagnosis of Train Body Sign Abnormal Pattern with Deep Learning Based Target Detection

Fault Diagnosis of Train Body Sign Abnormal Pattern with Deep Learning Based Target Detection

With the development of high-speed trains in recent years, security issues have received more attention. Automatic visual inspection of the train operation system for detecting abnormalities has become a fundamental element to guarantee the safety of the train operation. Train body sign patterns like the loss and fracture of signs and lock catch (SLC) on the electrical box cover (EBC) affect the regular operation of the train electrical system. In this paper, to ensure the safe operation of the train, a novel method combining a faster region-based convolutional neural network (Faster R-CNN) and similarity metrics is proposed to detect the abnormality of SLCs on train EBC. First, the positions of body train signs of multiple sizes are located by Faster R-CNN. Then, the regions of interest (ROI) are cut out and resized to the same size as the corresponding template images. Finally, by similarity measures, the status of the train body sign pattern is judged by comparing with the given threshold similarity value between ROIs and the template images. It is worth noting that the combination of Faster R-CNN and cosine similarity renders high accuracy in small target detection and strong robustness in image similarity comparison. The effectiveness of the proposed fault detection method and its superiority over the other types of combined methods are verified by actual experiments on the train of Guangzhou Metro Line 2.