Application of YOLOv7 in GPR B-Scan image interpretation

doi:10.13474/j.cnki.11-2246.2023.0227

Abstract

Abstract: Ground penetrating radar technology has been widely used in tunnel lining disease detection, for ground penetrating radar B-Scan image interpretation, many deep learning algorithms have emerged in recent years, but YOLOv7 algorithm has not been applied in this field. In this paper, the cavities and leakage water diseases of tunnel lining are simulated through FDTD forward performance, and 12 measuring lines in the tunnel are collected on the spot to analyze their disease distribution, so as to form the tunnel lining disease dataset, and then, based on the YOLOv7 algorithm, different image features of the two types of diseases are used to realize the automatic interpretation of tunnel lining diseases. The results show that the target detection algorithm of YOLOv7 is used to identify the overall disease with accuracy and recall rate of 97.87% and 90.61%, respectively. When the threshold IoU is 0.5, the identification accuracy of cavitation disease and leakage water disease is 97.2% and 96.4%, respectively. Finally, 100 images were randomly selected for testing, and the accuracy of the test results reached 94%. The final experimental identification results can be well used in production projects.

Key words: YOLOv7, Tunnel lining diseases, ground penetrating radar(GPR), deep learning, FDTD forward simulations

CLC Number:

HU Rongming, LI Xin, JING Xia, WU Jianqiang, WEI Qingbo. Application of YOLOv7 in GPR B-Scan image interpretation[J]. Bulletin of Surveying and Mapping, 2023, 0(8): 29-33.

References

[1] 李尧,李术才,徐磊,等.隧道衬砌病害地质雷达探测正演模拟与应用[J].岩土力学,2016,37(12):3627-3634.
[2] GUO Haonan,SHI Qian,DU Bo,et al.Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,2021,59(5):4287-4306.
[3] 邵延华,张铎,楚红雨,等.基于深度学习的YOLO目标检测综述[J].电子与信息学报,2022,44(10):3697-3708.
[4] 袁珑,李秀梅,潘振雄,等.面向目标检测的对抗样本综述[J].中国图象图形学报,2022,27(10):2873-2896.
[5] 张东昊,覃晖.基于探地雷达和深度学习的隧道初期支护检测方法[J].现代隧道技术,2020,57(S1):174-178.
[6] QIN Hui,ZHANG Donghao,TANG Yu,et al.Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation[J].Automation in Construction,2021,130:103830.
[7] ZHAO Shuai,SHADABFAR M,ZHANG Dongming,et al.Deep learning-based classification and instance segmentation of leakage-area and scaling images of shield tunnel linings[J].Structural Control and Health Monitoring,2021,28(6):e2732.1-e2732.22.
[8] 任松,朱倩雯,涂歆玥,等.基于深度学习的公路隧道衬砌病害识别方法[J].浙江大学学报(工学版),2022,56(1):92-99.
[9] 彭雨诺,刘敏,万智,等.基于改进YOLO的双网络桥梁表观病害快速检测算法[J].自动化学报,2022,48(4):1018-1032.
[10] 朱家松,郑澳,雷占占,等.基于改进Yolov5的地铁隧道附属设施与衬砌表观病害检测方法[J].铁道科学与工程学报,2023,20(3):1008-1019.
[11] 冯德山,杨子龙.基于深度学习的隧道衬砌结构物探地雷达图像自动识别[J].地球物理学进展,2020,35(4):1552-1556.
[12] 周中,闫龙宾,张俊杰,等.基于YOLOX-G算法的隧道裂缝实时检测[J/OL].铁道科学与工程学报:1-12[2023-07-17].https://doi.org/10.19713/j.cnki.43-1423/u.T20221523.
[13] FEDIUK A,WUNDERLICH T,WILKEN D,et al.Ground penetrating radar measurements in shallow water environments-a case study[J].Remote Sensing,2022,14(15):3659.
[14] WANG C Y,BOCHKOVSKIY A,LIAO H Y M.YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL].[2022-10-18].https://arxiv.org/abs/2207.02696.

[1]	ZHOU Baoding, XIE Peiyao, GUO Wenhao, MAO Qingzhou. Detection of tunnel leakage based on gray scale image of laser point cloud [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 34-39,90.
[2]	ZHANG Jian, GAO Ya. Application of deep learning remote sensing image interpretation technology in cultivated land protection [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 142-145.
[3]	YANG Ying, QIU Qinjun, XIE Zhong, TIAN Miao, ZHENG Shiyu, ZHENG Shuai. Geographical named entity recognition based on human-in-the-loop learning enhancement [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 155-160,177.
[4]	ZHONG Jing, SUN Jie, LAI Zulong, SHEN Yifu. A deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets [J]. Bulletin of Surveying and Mapping, 2023, 0(7): 39-43.
[5]	SHEN Xinsu, JI Ling. The improved method of U-type deep learning neural networkfor remote sensing in land type change detection [J]. Bulletin of Surveying and Mapping, 2023, 0(6): 93-97,103.
[6]	CHEN Qiaoyi, YAN Yufei, HUANG Yongfang. Rural building information extraction using object-oriented and deep belief network [J]. Bulletin of Surveying and Mapping, 2023, 0(6): 134-137.
[7]	WANG Limei, WANG Yanzheng. Buildings extraction based on high-resolution remote sensing imagery [J]. Bulletin of Surveying and Mapping, 2023, 0(6): 180-183.
[8]	LI Xinyuan, HE Zhi, LOU Anjun, XIAO Man. Remote sensing classification of wetlands in regions around the South China Sea based on bilinear graph convolutional network [J]. Bulletin of Surveying and Mapping, 2023, 0(5): 44-50.
[9]	CAI Jun, WEN Riqiang, JIANG Wei, YAN Jiao, LU Lijuan. DeepLabV3+ improved algorithm for national building recognition in traditional village aerial images [J]. Bulletin of Surveying and Mapping, 2023, 0(4): 49-53.
[10]	QI Jianwei, WANG Weifeng, ZHANG Le, WANG Guangyan. Building change detection of remote sensing image based on improved DeepLabV3+ [J]. Bulletin of Surveying and Mapping, 2023, 0(4): 145-149.
[11]	SHE Zuoming, SHEN Yongzhi, SONG Jianhong, XIANG Yujin. Using the classical CNN network method to construct the automatic extraction model of remote sensing image of Guiyang road elements [J]. Bulletin of Surveying and Mapping, 2023, 0(4): 177-182.
[12]	WANG Benli, WANG Ye, TANG Xianlong, DONG Shengguang. Research and application an intelligent extraction platform of remote sensing image change detection [J]. Bulletin of Surveying and Mapping, 2023, 0(2): 150-154.
[13]	WU Zhenzhen, TANG Chao, YANG Xiaofei. Application of video recognition and dynamic monitoring technology based on deep learning: taking the rail transit construction project as an example [J]. Bulletin of Surveying and Mapping, 2022, 0(9): 23-28.
[14]	TIAN Youliang, FAN Tingli, TANG Chao. Rapid detection technology for surface water leakage in subway tunnel [J]. Bulletin of Surveying and Mapping, 2022, 0(9): 29-33.
[15]	QIAN Jianguo, ZHANG Yuqi, TANG Shengjun, WANG Weixi, LI Xiaoming. Indoor adherent point cloud segmentation method based on joint optimization of minimal cut and deep learning [J]. Bulletin of Surveying and Mapping, 2022, 0(9): 45-51.