Abstract
Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.
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References
Ali R, Cha YJ (2019) Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer. Construction and Building Materials 226(Nov.30):376–387, DOI: https://doi.org/10.1016/j.conbuildmat.2019.07.293
Beckman GH, Polyzois D, Cha YJ (2018) Deep learning-based automatic volumetric damage quantification using depth camera. Automation in Construction 99(MAR.):114–124, DOI: https://doi.org/10.1016/j.autcon.2018.12.006
Cha YJ, Choi W, Buyukozturk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Computer Aided Civil & Infrastructure Engineering 32(5):361–378, DOI: https://doi.org/10.1111/mice.12263
Cha YJ, Choi W, Suh G, Mahmoudkhani S, Buyukozturk O (2018) Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33(9):731–747, DOI: https://doi.org/10.1111/mice.12334
Chen FC, Jahanshahi MR (2018) NB-CNN: Deep learning-based crack detection using convolutional neural network and naïve bayes data fusion. IEEE Transactions on Industrial Electronics 65(5):4392–4400, DOI: https://doi.org/10.1109/TIE.2017.2764844
Chen XL, Li J, Huang SW, Cui H, Sun Q (2021) An automatic concrete crack-detection method fusing point clouds and images based on improved otsu’s algorithm. Sensors 21(5):1581, DOI: https://doi.org/10.3390/s21051581
Dong JX, Liu JH, Wang NN, Fang HY, Zhang JP, Hu HB, Ma D (2021) Intelligent segmentation and measurement model for asphalt road cracks based on modified mask R-CNN algorithm. Computer Modeling in Engineering & Sciences 128(2):541–564, DOI: https://doi.org/10.32604/cmes.2021.015875
Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Construction and Building Materials, 186:1031–1045, DOI: https://doi.org/10.1016/j.conbuildmat.2018.08.011
Dung CV, Sekiya H, Hirano S, Okatani T, Miki C (2019) A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks. Automation in Construction 102:217–229, DOI: https://doi.org/10.1016/j.autcon.2019.02.013
Eisenbach M, Stricker R, Seichter D, Amende K, Debes K, Sesselmann M, Ebersbach D, Stoeckert U, Gross H (2017) How to get pavement distress detection ready for deep learning?. A Systematic Approach. 2017 International Joint Conference on Neural Networks 2017:2039–2047, DOI: https://doi.org/10.1109/IJCNN.2017.7966101
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507, DOI: https://doi.org/10.1126/science.1127647
Hu WK, Deng H, Fu ZX, An DY, Duan R (2022) Bridge crack segmentation and measurement method based on full convolutional neural network. Industrial Construction 1–14, DOI: https://doi.org/10.13204/j.gyjzG21053111 (in Chinese)
Jang KY, Kim NG, An YK (2019) Deep learning-based autonomous concrete crack evaluation through hybrid image scanning. Structural Health Monitoring 18(5–6):1722–1737, DOI: https://doi.org/10.1177/1475921718821719
Jiang S, Zhang J (2020) Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system. Computer-Aided Civil and Infrastructure Engineering 35(6):549–564, DOI: https://doi.org/10.1111/mice.12519
Kadam KD, Ahirrao S, Kotecha K, Gupta SK (2022) Efficient approach towards detection and identification of copy move and image splicing forgeries using mask R-CNN with mobileNet V1. Computational Intelligence and Neuroscience, 2022, DOI: https://doi.org/10.1155/2022/6845326
Kang D, Benipal SS, Gopal DL, Cha YJ (2020) Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction 118:103291, DOI: https://doi.org/10.1016/j.autcon.2020.103291
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Communications of the ACM 60(6):84–90, DOI: https://doi.org/10.1145/3065386
Lin TY, Goyal P, Girshick R, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(2):318–327, DOI: https://doi.org/10.1109/TPAMI.2018.2858826
Liu ZQ, Cao YW, Wang YZ, Wang W (2019) Computer vision-based concrete crack detection using U-net fully convolutional networks. Automation in Construction 104:129–139, DOI: https://doi.org/10.1016/j.autcon.2019.04.005
Park H, Sjösund LL, Yoo YJ, Bang J, Kwak N (2019) Extremec3net: Extreme lightweight portrait segmentation networks using advanced c3-modules. ar**v preprint ar**v:1908.03093, DOI: https://doi.org/10.48550/ar**v.1908.03093
Protopapadakis E, Voulodimos A, Doulamis A, Doulamis N, Stathaki T (2019) Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Applied Intelligence 49(7):2793–2806, DOI: https://doi.org/10.1007/s10489-018-01396-y
Ramos JG, Dalmau O, Alarcon T (2022) U-Net based neural network for fringe pattern denoising. Optics and Lasers in Engineering 149, DOI: https://doi.org/10.1016/j.optlaseng.2021.106829
Ren Y, Huang J, Hong Z, Lu W, Yin J, Zou L, Shen X (2020) Image-based concrete crack detection in tunnels using deep fully convolutional networks. Construction and Building Materials 234(C), DOI: https://doi.org/10.1016/j.conbuildmat.2019.117367
Sha AM, Tong Z, Gao J (2018) Recognition and measurement of pavement disasters based on convolutional neural networks. China Journal of Highway and Transport 31(1):1–10, DOI: https://doi.org/10.19721/j.cnki.1001-7372.2018.01.001
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Science, DOI: https://doi.org/10.48550/ar**v.1409.1556
Song T, Pang SC, Hao SH, Patón AR, Zheng P (2019) A parallel image skeletonizing method using spiking neural P systems with weights. Neural Processing Letters 50(2):1485–1502, DOI: https://doi.org/10.1007/s11063-018-9947-9
Szegedy C, Lui W, Jia YQ, Sermanet P, Rabinovich A (2015) Going deeper with convolutions. Proc., 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: https://doi.org/10.1109/cvpr.2015.7298594
Tong Z, Gao J, Zhang H (2017) Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks. Construction and Building Materials 146:775–787, DOI: https://doi.org/10.1016/j.conbuildmat.2017.04.097
Yamane T, Chun PJ (2020) Crack detection from a concrete surface image based on semantic segmentation using deep learning. Journal of Advanced Concrete Technology 18(9):493–504, DOI: https://doi.org/10.3151/jact.18.493
Yang F, Zhang L, Yu S, Prokhorov D, Mei X, Ling H (2020a) Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems 21(4):1525–1535, DOI: https://doi.org/10.1109/TITS.2019.2910595
Yang JW, Zhang G, Chen XJ, Ban Y (2020b) Research on bridge crack detection based on deep learning under complex background. Journal of Railway Science and Engineering 17(11):2722–2728, DOI: https://doi.org/10.19713/j.cnki.43-1423/u.T20200107 (in Chinese)
Zhang SQ, Ma W, **ong YL, Ma JY, Chen C, Zhang YM, Li ZJ (2019) Ultrasonic monitoring of crack propagation of notched concretes using embedded piezo-electric transducers. Journal of Advanced Concrete Technology 17(8):449–461, DOI: https://doi.org/10.3151/jact.17.7.449
Zhang LX, Shen JK, Zhu BJ (2020) A research on an improved Unet-based concrete crack detection algorithm. Structural Health Monitoring 20(4):1864–1879, DOI: https://doi.org/10.1177/1475921720940068
Zhao ZQ, Zheng P, Xu ST, Wu XD (2019) Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems 30(11):3212–3232, DOI: https://doi.org/10.1109/TNNLS.2018.2876865
Acknowledgments
This study was funded by the Project of the Supported by Sichuan Science and Technology Program (2021YJ0038) and the National Natural Science Foundation of China (52078442). Their support is gratefully acknowledged.
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Meng, Q., Hu, L., Wan, D. et al. Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network. KSCE J Civ Eng 27, 5231–5242 (2023). https://doi.org/10.1007/s12205-023-0923-1
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DOI: https://doi.org/10.1007/s12205-023-0923-1