Intelligent lost and loose detection of track fastener components based on 3d camera

During railway operations, track fasteners may develop defects such as loosening, falling off, or breaking, which can impair the stability and safety of train travel. Regular and prompt inspection and maintenance of track fasteners is necessary. However, traditional manual inspections are inefficient and cannot keep up with the rapid development of China’s rail transit. Additionally, detecting defects such as loose components is challenging. Consequently, using computer vision to develop automated detection systems has become a trend. Among them, line structured-light technology based on the principle of triangulation is widely used due to its low cost, high precision, and fast speed, and is well-suited for rail inspection scenarios. The primary device utilized is a 3D camera that can capture and analyze line structured light information for 3D reconstruction. Based on the imaging principle, a fastener detection system was designed and installed on a rail inspection vehicle. High-quality image data and 3D model were obtained through data analysis and processing after field testing which is applied to verify inspecting performance. For image data, a dataset was created using object detection methods, and a YOLO (You Only Look Once) v5 deep learning model was used to achieve fast recognition of track shoulder and fastener components. For 3D model, the fastener data was filtered according to a threshold based on the fixed relative position. The coordinate information of components such as strips and bolts was further obtained. Loose components detection was performed through displacement calculating using methods such as plane fitting. The results indicate that the detection system can acquire high-quality fastener data. The detection average precision for fastener components loss can achieve 99.0% with a fast detection speed which means real-time on-site detection is feasible, and the accuracy of detecting looseness in strips and bolts reach 1mm and 0.1 mm, respectively. The proposed method holds significant practical engineering value, as it has the potential to substantially enhance track inspection efficiency. Notably, in instances of critical defects such as lost or loosening of fastener components, this approach can provide timely warnings and facilitate prompt repairs, ensuring optimal track safety performance. © 2024, Central South University Press. All rights reserved.