Mi Zengzhen, Chen Ren, Zhao Shanshan
College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China.
Front Neurorobot. 2023 Feb 9;17:1119896. doi: 10.3389/fnbot.2023.1119896. eCollection 2023.
The surface images of steel rails are extremely difficult to detect and recognize due to the presence of interference such as light changes and texture background clutter during the acquisition process.
To improve the accuracy of railway defects detection, a deep learning algorithm is proposed to detect the rail defects. Aiming at the problems of inconspicuous rail defects edges, small size and background texture interference, the rail region extraction, improved Retinex image enhancement, background modeling difference, and threshold segmentation are performed sequentially to obtain the segmentation map of defects. For the classification of defects, Res2Net and CBAM attention mechanism are introduced to improve the receptive field and small target position weights. The bottom-up path enhancement structure is removed from the PANet structure to reduce the parameter redundancy and enhance the feature extraction of small targets.
The results show the average accuracy of rail defects detection reaches 92.68%, the recall rate reaches 92.33%, and the average detection time reaches an average of 0.068 s per image, which can meet the real-time of rail defects detection.
Comparing the improved method with the mainstream target detection algorithms such as Faster RCNN, SSD, YOLOv3 and other algorithms, the improved YOLOv4 has excellent comprehensive performance for rail defects detection, the improved YOLOv4 model obviously better than several others in , , and F1 value, and can be well-applied to rail defect detection projects.
由于在采集过程中存在诸如光照变化和纹理背景杂乱等干扰，钢轨的表面图像极难检测和识别。
为提高铁路缺陷检测的准确性，提出一种深度学习算法来检测钢轨缺陷。针对钢轨缺陷边缘不明显、尺寸小以及背景纹理干扰等问题，依次进行钢轨区域提取、改进的Retinex图像增强、背景建模差分和阈值分割，以获得缺陷分割图。对于缺陷分类，引入Res2Net和CBAM注意力机制来扩大感受野并提高小目标位置权重。从PANet结构中移除自底向上路径增强结构，以减少参数冗余并增强小目标的特征提取。
结果表明，钢轨缺陷检测的平均准确率达到92.68%，召回率达到92.33%，平均检测时间达到每张图像平均0.068 s，能够满足钢轨缺陷检测的实时性要求。
将改进后的方法与Faster RCNN、SSD、YOLOv3等主流目标检测算法进行比较，改进后的YOLOv4在钢轨缺陷检测方面具有出色的综合性能，改进后的YOLOv4模型在准确率、召回率和F1值方面明显优于其他几种算法，并且能够很好地应用于钢轨缺陷检测项目。
