Foreign object detection for railway ballastless trackbeds: A semisupervised learning method

This paper proposes a semisupervised algorithm for detecting foreign objects in ballastless beds based on the improved deep SVDD (Support Vector Data Description) algorithm. First, we use the improved Mask R-CNN algorithm to extract the rail and fastener areas in images, assuming that no foreign object exists in the rail and fastener areas. Second, we deepen the backbone network of the deep SVDD to enhance its ability to extract deep semantics from complex images. We perform pure color coverage processing with different colors and mean blur processing with different blur kernels on the rails and fastener regions extracted by the improved Mask R-CNN. The results show that the AUC (Area Under the Curve) of our improved deep SVDD algorithm is 89.23% and improves the AUC compared to that of the original model by 11.09%.