Automated Defect Detection in Fasteners and Rails: A Review of Recent Advancements

In their study, Tu et al. developed a lightweight segmentation network and defects detection network to accurately segment and locate fasteners and rails. They further extended their approach to detect defects in fasteners and rails. This method enabled automated detection of defects without the need for manual intervention.

Similarly, Yang et al. put forth an end-to-end method for segmenting rail surface defects. Unlike traditional manual detection techniques, their approach enabled automatic detection of defects on rail surfaces. By eliminating the need for manual inspection, their method offered a more efficient and streamlined process for defect detection.

Overall, both studies focused on improving the efficiency and accuracy of defect detection in fasteners and rails. Tu et al. achieved this by developing dedicated networks for segmentation and defect detection, while Yang et al. proposed an end-to-end approach for rail surface defect segmentation. These advancements in automated defect detection hold great promise for enhancing the overall safety and maintenance of railways.