A Forward Train Detection Method Based on Convolutional Neural Network Forward train detection is of great significance to improve train safety. In this paper, a visionbased method is proposed for forward train detection. The proposed method based on CenterNet, a novel object detection network, to realize accurate and fast forward train detection. The forward train detection network is divided into two stages: downsampling stage and center points generation stage. In downsampling stage, a full convolution network is performed to downsample the image, meanwhile to extract the feature of the image. In the center points generation stage, three branch networks are used to predict the bounding box of the forward train, including heatmaps generation branch, center offset regression branch and forward train bounding box size regression branch. Experiments results show that the proposed method can detect the forward train well and achieve 30.7% Average Precision (AP) with a 47.6 Frames Per Second (FPS).

An EndtoEnd Abnormal Fastener Detection Method Based on Data Synthesis Fasteners that hold rails in a fixed position are essentially important infrastructure components, and abnormal fasteners may cause a train to derail. The periodical inspection of fasteners is a significant guarantee for the safety of railway operation, and machine vision systems are popularly applied for fastener inspection. In this paper, we present an endtoend abnormal fastener detection method, which identifies abnormal fasteners from a track image that contains a rail, fasteners, sleepers, etc. The proposed method is inspired from the wellestablished Faster RCNN, but customizes with two aspects according to the application of fastener inspection. On the one hand, a lightweight backbone network is employed instead of complicated network to quicken detecting speed, and a threshold pruning algorithm is designed to reduce false positive rate. On the other hand, a hybrid loss function, combining weighted softmax loss function with center loss function, is devised to handle both the problems of class imbalance and small interclass differences. Furthermore, two data synthesis methods are brought forward to solve the small sample problem. The proposed method is verified on a realworld image set of railway inspection, and the experimental results demonstrate that our method outperforms four established baselines.