Rural road crack segmentation method in complex scene

doi:10.13474/j.cnki.11-2246.2022.0144

Abstract

Abstract: Aiming at the problems of small number of training samples, single scene and inaccurate extraction results in rural road crack identification,based on the measured road image data of Liaoning province for many years,this paper constructs a pavement crack datasets (PCDs) with multiple types and scenes,using ResNet50 network as encoder and SegNet as decoder,a pavement crack image recognition network Res-SegNet is constructed,by increasing the size of the convolution kernel to obtain more abundant crack information,the Focal Loss function is used to makes the model more focused on difficult samples.The block prediction method is used to improve the proportion of fractures in the image and make the image prediction more precise.The network model and prediction method are compared:The test set of PCDs is identified by Res-SegNet in different scenes, the average F value is 0.691,Res-SegNet combined with block prediction is used to identify the test set of PCDs,and the average F value is 0.753 in different scenarios.

Key words: crack detection, deep learning, dataset, Res-SegNet model, block prediction

CLC Number:

ZHANG Jinhe, QIN Yuluo, ZHANG Zaiyan, SONG Weidong, ZHU Hongbo. Rural road crack segmentation method in complex scene[J]. Bulletin of Surveying and Mapping, 2022, 0(5): 74-78,88.

References

[1] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.
[2] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-98.
[3] PREWITT M S J.Object enhancement and extraction[J].Picture Processing&Psychopictorics,1970,15(3):75-149.
[4] ZOU Qin, CAO Yu, LI Qingquan, et al. CrackTree:Automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3):227-238.
[5] 张宏,英红.频域滤波的水泥路面图像降噪增强方法[J].土木建筑与环境工程, 2015, 37(3):48-52.
[6] 高建贞.基于图像分析的道路病害自动检测研究[D].南京:南京理工大学, 2003.
[7] GU Jiuxiang, WANG Zhenhua, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77:354-377.
[8] WANG Yanyan,SONG Kechen,LIU Jie,et al.RENet:Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks[J]. Measurement, 2021, 170:108698.
[9] JIA Guohui, SONG Weidong, JIA Di, et al. Sample generation of semi-automatic pavement crack labelling and robustness in detection of pavement diseases[J]. Electronics Letters, 2019, 55(23):1235-1238.
[10] SONG Weidong, JIA Guohui, ZHU Hong, et al. Automated pavement crack damage detection using deep multiscale convolutional features[J]. Journal of Advanced Transportation, 2020:6412562.
[11] LIU Zhenqing, CAO Yiwen, WANG Yize, et al. Computer vision-based concrete crack detection using U-net fully convolutional networks[J]. Automation in Construction, 2019, 104:129-139.
[12] 成斌,管海燕,季秋菊,等.车载LiDAR数据的道路裂缝信息自动提取[J].测绘科学, 2018, 43(8):130-134.
[13] XIA Guisong, HU Jingwen, HU Fan, et al. AID:a benchmark data set for performance evaluation of aerial scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(7):3965-3981.
[14] EISENBACH M, STRICKER R, SEICHTER D, et al. How to get pavement distress detection ready for deep learning?A systematic approach[C]//Proceedings of 2017 International Joint Conference on Neural Networks (IJCNN).Anchorage,AK,USA:IEEE,2017:2039-2047.
[15] SHI Yong, CUI Limeng, QI Zhiquan, et al. Automatic Road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(12):3434-3445.
[16] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas, NV:IEEE,2016:770-778.
[17] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495.
[18] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2):318-327.
[19] WANG K C P.Designs and implementations of automated systems for pavement surface distress survey[J].Journal of Infrastructure Systems,2000,6(1):24-32.
[20] DAVIS J, GOADRICH M. The relationship between precision recall andROC curves[C]//Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh:ACM,2006:233-240.