Modeling and Optimization of Semantic Segmentation for Track Bed Foreign Object Based on Attention Mechanism

The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways. To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation. Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image. The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects. To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized. Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category. The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces. The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%. The anomaly detection results on actual lines also verify the effectiveness of the method.