Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (6): 310-317.DOI: 10.3778/j.issn.1002-8331.2110-0419

• Engineering and Applications • Previous Articles     Next Articles

Surface Crack Detection in Ballastless Slab Track of High-Speed Railway Based on Improved RetinaNet

ZHANG Shihui, LUO Hui, PEI Yingling, YU Junying, XU Jie   

  1. 1.School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
    2.Jiangxi Huitong Technology Development Co., Ltd., Nanchang 330013, China
  • Online:2023-03-15 Published:2023-03-15



  1. 1.华东交通大学 信息工程学院,南昌 330013
    2.江西慧通科技发展有限责任公司,南昌 330013

Abstract: A crack detection method based on improved RetinaNet is proposed to address the problems of large difference between scales and unbalanced crack types in the surface of ballastless slab track of high-speed railway. To alleviate the problem of subtle information loss caused by downsampling and horizontal connection compression of feature pyramid, multi-level feature pyramid network is used to integrate different depth features extracted from ResNet-50 backbone network to achieve full expression of image feature information. To solve the problem of mismatching between the classification and positioning confidence of surface cracks in the detection process, adaptive anchor learning is proposed to optimize the anchor and the network model at the same time, which improves the detection accuracy of small-scale cracks. To alleviate the impact of crack category imbalance in detection performance, Focal Loss function is introduced as the classification loss function, and weight factor of class balance is added to improve the detection accuracy of small types of cracks. The experimental results show that the improved RetinaNet detection network achieves good results on different crack types in the ballastless slab track of high-speed railway, and the mean average precision(mAP) is 72.58%, which is 3.60 percentage points higher than that of the original RetinaNet detection network, effectively realizes the accurate detection of cracks of different scales.

Key words: object detection, ballastless slab track of high-speed railway, crack detection, RetinaNet, multi-level feature pyramid network(MLFPN), anchor, Focal Loss

摘要: 针对高铁无砟轨道板表面裂缝尺度差异大、裂缝类别不平衡等问题,提出了基于改进RetinaNet的裂缝检测方法。为了缓解下采样与特征金字塔横向连接压缩而导致的细微信息丢失的问题,利用多级特征金字塔融合ResNet-50主干网络中提取的不同层次的深浅特征,实现了图像特征信息的充分表达;为了解决检测过程中表面裂缝的分类和定位置信度之间不匹配的问题,提出自适应锚点学习使锚点与网络模型同时进行优化,提高了对小尺度裂缝的检测精度;为了缓解裂缝类别不平衡对检测性能的影响,引入焦点损失函数(Focal Loss)作为分类损失函数,并在其中添加类平衡权重项因子,提升了对小类别裂缝的检测精度。实验结果表明,改进RetinaNet检测网络对高铁无砟轨道板不同类别的裂缝均获得了较好的效果,平均检测精度(mAP)达到72.58%,较之原始RetinaNet检测网络提高了3.60个百分点,有效实现了对不同尺度裂缝的准确检测。

关键词: 目标检测, 高铁无砟轨道板, 裂缝检测, RetinaNet, 多级特征金字塔, 锚点, Focal Loss