Automatic road crack detection and analysis system based on deep feature fusion and edge structure extraction

References

• Ali, R., et al., 2022. Structural crack detection using deep convolutional neural networks. Automation in Construction, 133, 103989.
   Web of Science ®Google Scholar
• Ali, R., and Cha, Y.-J., 2022. Attention-based generative adversarial network with internal damage segmentation using thermography. Automation in Construction, 141, 104412.
   Web of Science ®Google Scholar
• Alom, M. Z., et al., 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955.
   Google Scholar
• Alom, M. Z., et al., 2019. Recurrent residual U-Net for medical image segmentation. J Med Imaging (Bellingham), 6 (1), 014006.
   PubMedGoogle Scholar
• Badrinarayanan, V., et al., 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), 2481–2495.
   PubMed Web of Science ®Google Scholar
• Bai, L., et al., 2021. The use of full-skip ultrasonic data and Bayesian inference for improved characterisation of crack-like defects. NDT & E International, 121, 102467.
   Web of Science ®Google Scholar
• Cha, Y.-J., et al., 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.
   Web of Science ®Google Scholar
• Cha, Y.-J., Choi, W., and Büyüköztürk, O., 2017. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32 (5), 361–378.
   Web of Science ®Google Scholar
• Chaiyasarn, K., et al., 2022. Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures. Automation in Construction, 140, 104388.
   Web of Science ®Google Scholar
• Fu, H., et al., 2018. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Transactions on Medical Imaging, 37 (7), 1597–1605.
   PubMed Web of Science ®Google Scholar
• Gavilán, M., et al., 2011. Adaptive road crack detection system by pavement classification. Sensors, 11 (10), 9628–9657.
   PubMed Web of Science ®Google Scholar
• Gehri, N., Mata-Falcón, J., and Kaufmann, W., 2020. Automated crack detection and measurement based on digital image correlation. Construction and Building Materials, 256, 119383.
   Web of Science ®Google Scholar
• Glišić, B., Posenato, D., and Inaudi, D. Integrity monitoring of an old steel bridge using fiber optic distributed sensors based on Brillouin scattering. ed. Nondestructive characterization for composite materials, aerospace engineering, civil infrastructure, and homeland security 2007, 2007, 65310P.
   Google Scholar
• Golding, V. P., et al., 2022. Crack detection in concrete structures using deep learning. Sustainability, 14 (13), 8117.
   Web of Science ®Google Scholar
• Han, H., et al., 2021. An advanced OTSU method integrated with edge detection and decision tree for crack detection in highway transportation infrastructure. Advances in Materials Science and Engineering, 2021, 1–12.
   Web of Science ®Google Scholar
• He, K., et al., 2016. Deep residual learning for image recognition. ed. Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
   Google Scholar
• He, K., Sun, J., and Tang, X., 2012. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (6), 1397–1409.
   Web of Science ®Google Scholar
• Hsieh, Y.-A., and Tsai, Y. J., 2020. Machine learning for crack detection: review and model performance comparison. Journal of Computing in Civil Engineering, 34 (5), 04020038.
   Web of Science ®Google Scholar
• Huyan, J., et al., 2022. Pixelwise asphalt concrete pavement crack detection via deep learning-based semantic segmentation method. Structural Control and Health Monitoring, 29 (8), e2974.
   Web of Science ®Google Scholar
• Kang, D., et al., 2020. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction, 118, 103291.
   Web of Science ®Google Scholar
• Kang, D. H., and Cha, Y. J., 2022. Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 21 (5), 2190–2205.
   PubMed Web of Science ®Google Scholar
• Kheradmandi, N., and Mehranfar, V., 2022. A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Construction and Building Materials, 321, 126162.
   Web of Science ®Google Scholar
• Khiem, N., and Lien, T., 2004. Multi-crack detection for beam by the natural frequencies. Journal of Sound and Vibration, 273 (1-2), 175–184.
   Web of Science ®Google Scholar
• Lecun, Y., Bengio, Y., and Hinton, G. J. N., 2015. Deep Learning, 521 (7553), 436.
   Google Scholar
• Li, G., et al., 2021. Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field. Measurement, 170, 108693.
   Web of Science ®Google Scholar
• Li, S., Cao, Y., and Cai, H., 2017. Automatic pavement-crack detection and segmentation based on steerable matched filtering and an active contour model. Journal of Computing in Civil Engineering, 31 (5), 04017045.
   Web of Science ®Google Scholar
• Liang, M., and Hu, X. 2015. Recurrent convolutional neural network for object recognition. ed. Proceedings of the IEEE conference on computer vision and pattern recognition, 3367–3375.
   Google Scholar
• Liu, J., et al., 2021. Characterization method of surface crack based on laser thermography. IEEE Access, 9, 76395–76402.
   Web of Science ®Google Scholar
• Luo, S., et al., 2022. Research on edge detection algorithm of work piece defect in machine vision detection system. 2022 IEEE 6th information technology and mechatronics engineering conference (ITOEC). 1231–1235.
   Google Scholar
• Maniat, M., Camp, C. V., and Kashani, A. R., 2021. Deep learning-based visual crack detection using google street view images. Neural Computing and Applications, 33 (21), 14565–14582.
   Web of Science ®Google Scholar
• Nhat-Duc, H., Nguyen, Q.-L., and Tran, V.-D., 2018. Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Automation in Construction, 94, 203–213.
   Web of Science ®Google Scholar
• Ouma, Y. O., and Hahn, M. J. A. E. I, 2016. Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular radon transform. Advanced Engineering Informatics, 30 (3), 481–499.
   Web of Science ®Google Scholar
• Pan, P., et al., 2022. Crack detection for nuclear containments based on multi-feature fused semantic segmentation. Construction and Building Materials, 329, 127137.
   Web of Science ®Google Scholar
• Ronneberger, O., Fischer, P., and Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation. ed. International conference on medical image computing and computer-assisted intervention, 234–241.
   Google Scholar
• Song, G., et al., 2006. Health monitoring and rehabilitation of a concrete structure using intelligent materials. Smart Materials and Structures, 15(2), 309.
   Google Scholar
• Xu, Z., et al., 2021. Pixel-level pavement crack detection using enhanced high-resolution semantic network. International Journal of Pavement Engineering, 23 (14), 4943–4957.
   Web of Science ®Google Scholar
• Yang, X., et al., 2018. Automatic pixel-level crack detection and measurement using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 33 (12), 1090–1109.
   Web of Science ®Google Scholar
• Yang, F., et al., 2019. Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems, 21 (4), 1525–1535.
   Web of Science ®Google Scholar
• Zhai, J., et al., 2022. Automatic pavement crack detection using multimodal features fusion deep neural network. International Journal of Pavement Engineering, 105 (4), 1–14.
   Google Scholar
• Zhang, L., et al., 2016. Road crack detection using deep convolutional neural network. ed. 2016 IEEE international conference on image processing (ICIP). 3708–3712.
   Google Scholar
• Zhang, A., et al., 2019a. Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34 (3), 213–229.
   Web of Science ®Google Scholar
• Zhang, S., et al., 2019b. Attention guided network for retinal image segmentation. ed. International conference on medical image computing and computer-assisted intervention. 797–805.
   Google Scholar
• Zhang, L., Shen, J., and Zhu, B., 2020. A research on an improved Unet-based concrete crack detection algorithm. Structural Health Monitoring, 20 (4), 1864–1879.
   Web of Science ®Google Scholar
• Zhou, P., et al., 2020. Generate, segment, and refine: Towards generic manipulation segmentation. ed. Proceedings of the AAAI conference on artificial intelligence. 13058–13065.
   Google Scholar
• Zou, Q., et al., 2018. Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28 (3), 1057–7149.
   Web of Science ®Google Scholar