Abstract

The speed and load capacity of trains are improving at an accelerating rate, which raises the standards for railway services' security criteria. The track's surface may progressively reveal varying degrees of defects because of the impacts of moisture, temperature, load, and other factors; if the flaws never addressed in a prompt period, the degree of defects would expand, significantly raising the probability of train service. The development of automatic RSDD (Rail Surface Defect Detection) has substantial practical and scientific implications. In this study, a multi-crack detection technique depends on Variational Autoencoder Diffusion Model (VAEDM), The introduction of VAEDM allows for the non-destructive detection of fastener and RSD. In order to produce hidden elements from images that have various cracks, VAEDM combines the multi-layer and a rail surface image encoder was introduced. Zero Shot- Divergence Asynchronous Reinforcement Learning (ZS-DARL), Policy-Gradient is used to establish the relationship between defects and non-defects. DARL transformer encoder is introduced for taking long-range dependences for FE (Feature Extraction) from detected objects images with various patterns and dimensions. The simulation outcomes on the open Railway Track Fault Detection (RTFD) and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection. Results are measured using the metrics like recall, precision, F-measure, and accuracy.