Method for Bridge Crack Detection Based on Multiresolution Network

Liangfu Li; Biao Wu; Nan Wang

doi:10.3788/LOP202158.1210004

[1] Mao W Q, Hu X W. Latest developments and prospects for long-span bridges in China[J]. Bridge Construction, 50, 13-19(2020).

[2] Abdel-Qader I, Pashaie-Rad S, Abudayyeh O et al. PCA-based algorithm for unsupervised bridge crack detection[J]. Advances in Engineering Software, 37, 771-778(2006). http://www.sciencedirect.com/science/article/pii/S0965997806000809

[3] Bu G P, Chanda S, Guan H et al. Crack detection using a texture analysis-based technique for visual bridge inspection[J]. Electronic Journal of Structural Engineering, 14, 41-48(2015). http://www.researchgate.net/publication/283110604_Crack_detection_using_a_texture_analysis-based_technique_for_visual_bridge_inspection

[4] Li H T, Chen X D, Xu H Y et al. Bridge crack detection algorithm based on Bilateral-Frangi filter[J]. Laser & Optoelectronics Progress, 56, 181401(2019).

[5] He F Q, Luo H, Ping A et al. Bridge crack extraction algorithm based on Hessian matrix enhancement and local crack connection[J]. Journal of Guizhou University (Natural Sciences), 37, 69-75(2020).

[6] Wang G Z. Pavement crack extraction based on path morphology[D], 20-28(2018).

[7] Ma Y, Wang G J. Detection for bridge surface crack based on 2D complex discrete wavelet packet transform[J]. Journal of Shenyang University of Technology, 40, 659-663(2018).

[8] Zhao F, Zhou W H, Chen Y T et al. Application of improved Canny operator in crack detection[J]. Electronic Measurement Technology, 41, 107-111(2018).

[9] Han K, Han H F. Pavement crack detection method based on region-level and pixel-level features[J]. Journal of Railway Science and Engineering, 15, 1178-1186(2018).

[10] Zhang L, Yang F, Daniel Z Y et al. Road crack detection using deep convolutional neural network[C]. //2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, AZ, USA., 3708-3712(2016).

[11] Chen F C, Jahanshahi M R. NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion[J]. IEEE Transactions on Industrial Electronics, 65, 4392-4400(2018). http://ieeexplore.ieee.org/document/8074762/citations

[12] Zhu S Y, Du J C, Li Y S et al. Method for bridge crack detection based on the U-Net convolutional networks[J]. Journal of Xidian University, 35-42(2019).

[13] Fan R, Bocus M J, Zhu Y L et al. Road crack detection using deep convolutional neural network and adaptive thresholding[C]. //2019 IEEE Intelligent Vehicles Symposium (IV), June 9-12, 2019, Paris, France., 474-479(2019).

[14] Xu H Y, Su X, Xu H Y et al. Autonomous bridge crack detection using deep convolutional neural networks[C]. //Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019), May 30-31, 2019, Chongqing, China.(2019).

[15] Liang X H, Cheng Y Z, Zhang R J et al. Bridge crack classification and measurement method based on deep convolutional neural network[J]. Journal of Computer Applications, 40, 1056-1061(2020).

[16] Wang J W, Yu P F, Luo H B. Classification method of railway bridge cracks based on improved Faster R-CNN+ZF model[J]. Journal of Beijing Jiaotong University, 44, 106-112(2020).

[17] Qing C, Yu J, Xiao C B et al. Deep convolutional neural network for semantic image segmentation[J]. Journal of Image and Graphics, 25, 1069-1090(2020).

[18] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[19] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[20] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018). http://europepmc.org/abstract/MED/28463186

[21] Sun K, Xiao B, Liu D et al. Deep high-resolution representation learning for human pose estimation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 5686-5696(2019).

[22] Sze V, Chen Y H, Yang T J et al. Efficient processing of deep neural networks: a tutorial and survey[J]. Proceedings of the IEEE, 105, 2295-2329(2017). http://ieeexplore.ieee.org/document/8114708

[23] Li L F, Hu M. Method for small-bridge-crack segmentation based on generative adversarial network[J]. Laser & Optoelectronics Progress, 56, 101004(2019).

[24] Li L F, Sun R Y. Bridge crack detection algorithm based on image processing under complex background[J]. Laser & Optoelectronics Progress, 56, 061002(2019).