Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0615006(2023)
FastCrack: Real-Time Pavement Crack Segmentation
[1] Mei Q P, Gül M. A cost effective solution for pavement crack inspection using cameras and deep neural networks[J]. Construction and Building Materials, 256, 119397(2020).
[2] Miao P Y, Srimahachota T. Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques[J]. Construction and Building Materials, 293, 123549(2021).
[3] Chen X D, Ai D H, Zhang J C et al. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 13, 1293-1301(2020).
[4] Liu Y H, Yao J, Lu X H et al. DeepCrack: a deep hierarchical feature learning architecture for crack segmentation[J]. Neurocomputing, 338, 139-153(2019).
[5] Oliveira H, Correia P L. Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 14, 155-168(2013).
[6] Faghih-Roohi S, Hajizadeh S, Núñez A et al. Deep convolutional neural networks for detection of rail surface defects[C], 2584-2589(2016).
[7] Zou Q, Cao Y, Li Q Q et al. CrackTree: automatic crack detection from pavement images[J]. Pattern Recognition Letters, 33, 227-238(2012).
[8] Guan H Y, Li J, Yu Y T et al. Iterative tensor voting for pavement crack extraction using mobile laser scanning data[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1527-1537(2015).
[9] Zhao H L, Qin G F, Wang X J. Improvement of Canny algorithm based on pavement edge detection[C], 964-967(2010).
[10] Li P, Wang C, Li S M et al. Research on crack detection method of airport runway based on twice-threshold segmentation[C], 1716-1720(2015).
[11] Li L F, Wu B, Wang N. Method for bridge crack detection based on multiresolution network[J]. Laser & Optoelectronics Progress, 58, 1210004(2021).
[12] Yan H, Zhao Q F, Xie M et al. Edge detection and repair of PCBA components based on adaptive Canny operator[J]. Acta Optica Sinica, 41, 0515003(2021).
[13] Shi Y, Cui L M, Qi Z Q et al. Automatic Road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 17, 3434-3445(2016).
[14] Jiang W B, Liu M, Peng Y N et al. HDCB-net: a neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges[J]. IEEE Transactions on Industrial Informatics, 17, 5485-5494(2021).
[15] Li G, Liu Q W, Wan J et al. A novel pavement crack detection algorithm using interlaced low-rank group convolution hybrid deep network under a complex background[J]. Laser & Optoelectronics Progress, 57, 141031(2020).
[16] Liu D, Zhang Y, Zhao Y et al. Multi-scale inshore ship detection based on feature re-focusing network[J]. Acta Optica Sinica, 41, 2215001(2021).
[17] Song Z Z, Yang J W, Zhang D F et al. Low-altitude sea surface infrared object detection based on unsupervised domain adaptation[J]. Acta Optica Sinica, 42, 0415001(2022).
[18] Mao Y C, Tang J H, Wang J et al. Multi-task enhanced dam crack image detection based on Faster R-CNN[J]. CAAI Transactions on Intelligent Systems, 16, 286-293(2021).
[19] Zhao L, Hu J, Liu H et al. Deep learning based on semantic segmentation for three-dimensional object detection from point clouds[J]. Chinese Journal of Lasers, 48, 1710004(2021).
[20] Zhang S K, Wu Q X, Lin Z Y. Detection and segmentation of structured light stripe in weld image[J]. Acta Optica Sinica, 41, 0515002(2021).
[21] Zhang A, Wang K C P, Li B X et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network[J]. Computer-Aided Civil and Infrastructure Engineering, 32, 805-819(2017).
[22] Yang X C, Li H, Yu Y T et al. Automatic pixel-level crack detection and measurement using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering, 33, 1090-1109(2018).
[23] Shim S, Cho G C. Lightweight semantic segmentation for road-surface damage recognition based on multiscale learning[J]. IEEE Access, 8, 102680-102690(2020).
[24] Choi W, Cha Y J. SDDNet: real-time crack segmentation[J]. IEEE Transactions on Industrial Electronics, 67, 8016-8025(2020).
[25] Shim S, Kim J, Lee S W et al. Road surface damage detection based on hierarchical architecture using lightweight auto-encoder network[J]. Automation in Construction, 130, 103833(2021).
[26] Guo Z C, Zhang X Y, Mu H Y et al. Single path one-shot neural architecture search with uniform sampling[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12361, 544-560(2020).
[29] Zhang A, Wang K C P, Fei Y et al. Deep learning-based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet[J]. Journal of Computing in Civil Engineering, 32, 4018041(2018).
[30] Chen X, Xie L X, Wu J et al. Progressive differentiable architecture search: bridging the depth gap between search and evaluation[C], 1294-1303(2019).
[33] Zagoruyko S, Komodakis N. Wide residual networks[C], 87.1-87.12(2016).
[34] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[35] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).
[38] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11218, 122-138(2018).
[39] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).
[40] Yang F, Zhang L, Yu S J et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 21, 1525-1535(2020).
[41] Zou Q, Zhang Z, Li Q Q et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 28, 1498-1512(2019).
[42] Xie S N, Tu Z W. Holistically-nested edge detection[C], 1395-1403(2015).
[43] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).
[44] 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, 39, 2481-2495(2017).
[45] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).
[46] Mehta S, Rastegari M, Caspi A et al. ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11214, 561-580(2018).
[47] Poudel R P K, Liwicki S, Cipolla R. Fast-SCNN: fast semantic segmentation network[C](2019).
[48] Li H C, Xiong P F, Fan H Q et al. DFANet: deep feature aggregation for real-time semantic segmentation[C], 9514-9523(2019).
[49] Ren Y P, Huang J S, Hong Z Y et al. Image-based concrete crack detection in tunnels using deep fully convolutional networks[J]. Construction and Building Materials, 234, 117367(2020).
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Zhuang Yue, Xiaodong Chen, Yi Wang, Huaiyu Cai, Weixi Yan, Liying Hou. FastCrack: Real-Time Pavement Crack Segmentation[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0615006
Category: Machine Vision
Received: Feb. 17, 2022
Accepted: Mar. 30, 2022
Published Online: Mar. 7, 2023
The Author Email: Xiaodong Chen (xdchen@tju.edu.cn)