Automatic detection and localization of surface defects in high-speed railway ballastless track based on cascaded group attention and optoelectronic encoder

Surface defects in ballastless tracks pose potential risks to the safety and stability of high-speed railway operations. However, the complex detection environment at night often leads to low computational efficiency and detection accuracy in structural damage assessment. To address these issues, this article proposes a lightweight detection and localization method for surface damage in ballastless tracks, deployed on an intelligent track inspection vehicle. The method enhances the network model’s ability to extract shallow and deep damage features under nighttime conditions, while mitigating the harmful gradient effects caused by low-quality samples. Besides, the method uses photoelectric encoding technology to achieve precise mileage localization of surface defects. Compared to the You Only Look Once (YOLO)v7 model, the YOLO-Track model achieves a precision of 98.2%, reflecting a 7.68% improvement, and reduces the parameter size by 33.80%, down to 23.05M. The YOLO-Track model with a dynamic nonmonotonic mechanism loss function achieves a mean average precision @0.75 of 81.4%, surpassing the traditional complete intersection over union loss function model by 13.06%. Finally, the effectiveness of the proposed method is validated through field tests.