基于深度学习的隧道衬砌多病害检测算法

Deep learning-based algorithm for multi defect detection in tunnel lining

针对已有目标检测算法在隧道衬砌病害检测中全局信息提取不充分、检测精度低的问题,提出隧道衬砌表观病害检测算法TDD-YOLO.该算法以YOLOv7框架为基础,采用MobileViT作为主干特征提取网络,提高网络全局信息和局部信息提取能力;在特征金字塔网络的上采样和下采样后增加Coordinate attention (CA)注意力模块,突出病害的特征信息,去除背景信息的干扰;提出卷积模块TP Block,在计算量较小的情况下进一步提高网络的特征提取能力.为了验证所提出算法的有效性,选用SSD、 Faster-RCNN、 EfficientDet、 YOLOv5、 YOLOv7这5种算法进行对比分析.实验结果表明,TDD-YOLO算法的F1为77.43%,相对5种对比算法,分别提高了15.58%、17.36%、 12.19%、 6.32%、 6.14%;mAP为77.52%,相对5种对比算法,分别提高了15.20%、 14.24%、 9.44%、 7.44%、6.39%. TDD-YOLO算法病害识别精度最高,综合性能最优,适用于实际隧道工程的病害检测任务.

A tunnel lining surface defect detection algorithm TDD-YOLO was proposed,for the problems of insufficient global information extraction and low detection accuracy of existing object detection algorithms in tunnel lining defect detection.The algorithm was based on the YOLOv7 framework.Firstly,MobileViT was used as the backbone feature extraction network to improve the global and local information extraction capability of the network.Secondly,Coordinate attention(CA)module was added after the upsampling and downsampling of the feature pyramid network to highlight the feature information of defects and remove the interference of background information.Finally,a convolutional module called TP Block was proposed to further improve the feature extraction ability of the network with less computation.Five algorithms,SSD,Faster-RCNN,EfficientDet,YOLOv5 and YOLOv7,were selected for comparison and analysis,in order to verify the effectiveness of the proposed algorithm.Results showed that the F1 of TDD-YOLO algorithm was 77.43%,which had an improvement of 15.58%,17.36%,12.19%,6.32%,and 6.14%,respectively,compared with the above five contrast algorithms.The mAP was 77.52%,which had an improvement of 15.20%,14.24%,9.44%,7.44%,and 6.39%,respectively.The TDD-YOLO algorithm has the highest defect recognition accuracy and the best overall performance,which is suitable for defect detection task of actual tunnel projects.