Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects

Abstract

In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE+

“…By leveraging the power of neural networks to extract features directly from raw data, deep learning-based detection algorithms have markedly enhanced both efficiency and accuracy. These algorithms are broadly classified into two types: two-stage methods, which prioritize candidate regions and are notable for their precision, including mask region convolutional neural networks (Mask R-CNNs) [3], spatial pyramid pooling networks (SPPNets) [4], Fast R-CNNs [5], Faster R-CNNs [6], and regionbased fully convolutional networks (R-FCNs) [7], though with considerable computational complexity; and one-stage methods, characterized by their simplicity, speed, and broader applicability but with somewhat lower accuracy, encompassing approaches such as singleshot detectors (SSDs) [8], YOLO [9], YOLOv2 [10], YOLOv3 [11], and YOLOv4 [12].…”