Rail surface crack detection algorithm based on improved YOLOv5s

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Rail surface crack detection algorithm based on improved YOLOv5s

Image Processing | 更新时间：2023-05-09

- Rail surface crack detection algorithm based on improved YOLOv5s
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 5, Pages: 666-679(2023)
- 作者机构：
  
  1.宁夏大学物理与电子电气工程学院，宁夏银川 750021
  2.宁夏沙漠信息智能感知重点实验室，宁夏银川 750021
- 作者简介：
- 基金信息：
  
  Collaborative education project of the Ministry of Education(202002056054)
- DOI：10.37188/CJLCD.2022-0267
  CLC： TP391.4
- Received：13 August 2022，
  
  Revised：06 September 2022，
  
  Published：05 May 2023
- 稿件说明：
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ZHOU Miao-sen, TANG Quan-wu, SHI Tian-tian, et al. Rail surface crack detection algorithm based on improved YOLOv5s[J]. Chinese journal of liquid crystals and displays, 2023, 38(5): 666-679.
DOI：

ZHOU Miao-sen, TANG Quan-wu, SHI Tian-tian, et al. Rail surface crack detection algorithm based on improved YOLOv5s[J]. Chinese journal of liquid crystals and displays, 2023, 38(5): 666-679. DOI： 10.37188/CJLCD.2022-0267.

摘要

铁轨轨枕表面出现的裂纹可能对轨道交通造成安全隐患。针对铁轨裂纹检测的方法存在通用性差、精度低、召回率低的问题，提出一种基于改进YOLOv5s的铁轨裂纹检测算法YOLOv5s-CBE。首先将CA注意力模块分别加入主干C3模块以及C3与SPPF之间，从通道和空间两个维度捕获通道关系和位置信息，提高YOLOv5s主干网络特征提取能力。其次，在YOLOv5s的Neck部分，使用BiFPN融合不同尺度信息，获取拥有丰富语义信息的输出特征图；同时，加权双向特征融合金字塔结构通过引入权重调整不同尺度输入特征图对输出的贡献，优化特征融合效果，减少了卷积过程中特征信息的丢失，提高了检测精度。最后，将原YOLOv5s中损失函数CIoU改为EIoU。EIoU不仅考虑了中心点距离和纵横比，而且还考虑了预测框与真实框宽度和高度的真实差异，提高了锚框的预测精度。相较于原始网络YOLOv5s，YOLOv5s-CBE铁轨裂纹检测网络在自制铁轨裂纹数据集上，模型大小相较于基础网络YOLOv5s降低了1.0 MB，精度mAP提高了3.7%，召回率由73.5%提升到76.2%，不同尺寸裂纹的漏检现象减少，具有一定的优越性和实用价值。

Abstract

Cracks on the surface of rail sleepers may cause hidden dangers to rail transit. Aiming at the problems of poor universality， low accuracy and low recall of rail crack detection methods， a rail crack detection algorithm YOLOv5s-CBE based on improved YOLOv5s is proposed. Firstly， the CA attention module is added to the backbone C3 module and between C3 and SPPF respectively to capture the channel relationship and location information from the two dimensions of channel and space， so as to improve the feature extraction capability of YOLOv5s backbone network. Secondly， in the neck part of YOLOv5s， BiFPN is used to fuse different scale information to obtain the output feature map with rich semantic information； At the same time， the weighted bi-directional feature fusion pyramid structure adjusts the contribution of input feature maps of different scales to the output by introducing weights， optimizes the feature fusion effect， reduces the loss of feature information in the convolution process， and improves the detection accuracy. Finally， the loss function CIoU in the original yolov5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the center point， but also considers the real difference in width and height between the prediction frame and the real frame， which improves the prediction accuracy of the anchor frame. Compared with the original network YOLOv5s， the model size of YOLOv5s-CBE rail crack detection network on the self-made rail crack data set is reduced by 1.0 MB， the accuracy mAP is increased by 3.7%， the recall rate is increased from 73.5% to 76.2%， and the phenomenon of missing detection of cracks of different sizes is reduced. It has certain advantages and practical value.

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references

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