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Articles

Automatic pavement crack detection based on single stage salient-instance segmentation and concatenated feature pyramid network

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Pages 4206-4222 | Received 24 Dec 2020, Accepted 28 May 2021, Published online: 11 Jun 2021

References

  • Ai, D., et al., 2018. Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods. IEEE Access, 6, 24452–24463.
  • Billah, U.H., et al., 2019. Classification of concrete crack using deep residual networked. In Proceedings of the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9), St. Louis, MO, USA, 4-7.
  • 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.
  • Cha, Y.-J., and Wang, Z, 2018. Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Structural Health Monitoring, 17 (2), 313–324.
  • Chen, F.-C., and Jahanshahi, M.R, 2017. 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.
  • Cord, A., and Chambon, S, 2012. Automatic road defect detection by textural pattern recognition based on adaboost. Computer-Aided Civil and Infrastructure Engineering, 27 (4), 244–259.
  • Fan, R., et al., 2019. S4net: single stage salient-instance segmentationed. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6103-6112.
  • Fan, Z., et al., 2020a. Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. Coatings, 10 (2), 152.
  • Fan, Z., et al., 2020b. Automatic crack detection on road pavements using encoder-decoder architecture. Materials, 13 (13), 2960.
  • Fujita, Y., and Hamamoto, Y, 2011. A robust automatic crack detection method from noisy concrete surfaces. Machine Vision and Applications, 22 (2), 245–254.
  • Fujita, Y., Mitani, Y., and Hamamoto, Y., 2006. A method for crack detection on a concrete structured. In 18th International Conference on Pattern Recognition (ICPR'06) IEEE, 901–904.
  • Girshick, R., 2015. Fast r-CNNed. In Proceedings of the IEEE international conference on computer vision, 1440-1448.
  • He, K., et al., 2016. Deep residual learning for image recognitioned. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
  • He, K., et al., 2017. Mask r-CNNed. In Proceedings of the IEEE international conference on computer vision, 2961–2969.
  • 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.
  • Hu, Y., Zhao, C., and Wang, H, 2010. Automatic pavement crack detection using texture and shape descriptors. Iete Technical Review, 27 (5), 398.
  • Huyan, J., et al., 2020. Cracku-net: A novel deep convolutional neural network for pixelwise pavement crack detection. Structural Control and Health Monitoring, 27 (8), e2551.
  • Ibragimov, E., et al., 2020. Automated pavement distress detection using region based convolutional neural networks. International Journal of Pavement Engineering, 1–12.
  • Inkoom, S., et al., 2019. Pavement crack rating using machine learning frameworks: partitioning, bootstrap forest, boosted trees, naïve Bayes, and k-nearest neighbors. Journal of Transportation Engineering, Part B: Pavements, 145 (3), 04019031.
  • Jahanshahi, M.R., and Masri, S.F, 2012. Adaptive vision-based crack detection using 3d scene reconstruction for condition assessment of structures. Automation in Construction, 22, 567–576.
  • Jégou, S., et al., 2017. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentationed. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 11–19.
  • Kalfarisi, R., Wu, Z.Y., and Soh, K, 2020. Crack detection and segmentation using deep learning with 3d reality mesh model for quantitative assessment and integrated visualization. Journal of Computing in Civil Engineering, 34 (3), 04020010.
  • Kim, J.-T., and Stubbs, N, 2003. Crack detection in beam-type structures using frequency data. Journal of Sound and Vibration, 259 (1), 145–160.
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E, 2012. Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, 25, 1097–1105.
  • Lecun, Y., Bengio, Y., and Hinton, G, 2015. Deep learning. Nature, 521 (7553), 436–444.
  • Liu, Z., et al., 2019. A two-stage approach for automated prostate lesion detection and classification with mask R-CNN and weakly supervised deep neural network.
  • Long, J., Shelhamer, E., and Darrell, T., 2015. Fully convolutional networks for semantic segmentationed. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440.
  • Mei, Q., Gül, M., and Azim, M.R, 2020. Densely connected deep neural network considering connectivity of pixels for automatic crack detection. Automation in Construction, 110, 103018.
  • Ni, T., et al., 2020. Measurement of concrete crack feature with android smartphone app based on digital image processing techniques. Measurement, 150, 107093.
  • Nishikawa, T., et al., 2012. Concrete crack detection by multiple sequential image filtering. Computer-Aided Civil and Infrastructure Engineering, 27 (1), 29–47.
  • Owolabi, G., Swamidas, A., and Seshadri, R, 2003. Crack detection in beams using changes in frequencies and amplitudes of frequency response functions. Journal of Sound and Vibration, 265 (1), 1–22.
  • Prasanna, P., et al., 2016. Automated crack detection on concrete bridges. IEEE Transactions on Automation Science and Engineering, 13 (2), 591–599.
  • Protopapadakis, E., et al., 2016. Crack identification via user feedback, convolutional neural networks and laser scanners for tunnel infrastructuresed. VISIGRAPP (4: VISAPP), 725–734.
  • Ren, S., et al., 2015. Faster R-CNN: towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 39(6), 91–99.
  • Ren, Y., et al., 2020. Image-based concrete crack detection in tunnels using deep fully convolutional networks. Construction and Building Materials, 234, 117367.
  • Salman, M., et al., 2013. Pavement crack detection using the gabor filtered. In 16th international IEEE conference on intelligent transportation systems (ITSC 2013) IEEE, 2039-2044.
  • Simonyan, K., and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. Computer Science, 1409.1556.
  • Sun, Y., et al., 2019. Concatenated feature pyramid network for instance segmentationed. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) IEEE, 297-301.
  • Szegedy, C., et al., 2015. Going deeper with convolutionsed. Computer Vision and Pattern Recognition, 1–9.
  • Tran, T.S., et al., 2020. A two-step sequential automated crack detection and severity classification process for asphalt pavements. International Journal of Pavement Engineering, 1–15.
  • Unger, J.F., Teughels, A., and De Roeck, G., 2005. Damage detection of a prestressed concrete beam using modal strains. Journal of Structural Engineering, 131 (9), 1456–1463.
  • Wang, M., and Cheng, J.C., 2018. Development and improvement of deep learning based automated defect detection for sewer pipe inspection using faster R-CNNed. In Workshop of the European Group for Intelligent Computing in Engineering. Springer, 171–192.
  • 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.
  • Zhang, L., et al., 2016. Road crack detection using deep convolutional neural networked. In IEEE international conference on image processing (ICIP) IEEE, 3708–3712.
  • Zhang, A., et al., 2019. 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.
  • Zhao, H., et al., 2017. Pyramid scene parsing networked. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2881–2890.
  • Zou, Q., et al., 2012. Cracktree: automatic crack detection from pavement images. Pattern Recognition Letters, 33 (3), 227–238.
  • Zou, Q., et al., 2018. Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28 (3), 1498–1512.

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