112
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A complex scene pavement crack semantic segmentation method based on dual-stream framework

, , &
Article: 2286461 | Received 22 Aug 2023, Accepted 17 Nov 2023, Published online: 12 Dec 2023

References

  • Choi, W., and Cha, Y.-J., 2019. SDDNet: real-time crack segmentation. IEEE Transactions on Industrial Electronics, 67 (9), 8016–8025.
  • Fan, L., et al., 2023. Pavement cracks coupled with shadows: a new shadow-crack dataset and a shadow-removal-oriented crack detection approach. IEEE/CAA Journal of Automatica Sinica, 10 (7), 1593–1607. doi:10.1109/JAS.2023.123447.
  • Fang, J., Qu, B., and Yuan, Y., 2021. Distribution equalization learning mechanism for road crack detection. Neurocomputing, 424, 193–204. doi:10.1016/j.neucom.2019.12.057.
  • Fei, Y., et al., 2019. Pixel-level cracking detection on 3D asphalt pavement images through deep-learning- based CrackNet-V. IEEE Transactions on Intelligent Transportation Systems, 21 (1), 273–284. doi:10.1109/TITS.2019.2891167.
  • Guo, C., et al., 2020. Augfpn: improving multi-scale feature learning for object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 12595–12604.
  • Han, C., et al., 2021. CrackW-Net: a novel pavement crack image segmentation convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 23 (11), 22135–22144. doi:10.1109/TITS.2021.3095507.
  • Hu, J., Shen, L., and Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 7132–7141.
  • Huang, X., et al., 2020. Intelligent intersection: two-stream convolutional networks for real-time near-accident detection in traffic video. ACM Transactions on Spatial Algorithms and Systems, 6 (2), 1–28. doi:10.1145/3373647.
  • Jenkins, M.D., et al., 2018. A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. In: 2018 26th European Signal Processing Conference (EUSIPCO), IEEE, 2120–2124.
  • Ji, A., et al., 2020. An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Automation in Construction, 114, 103176. doi:10.1016/j.autcon.2020.103176.
  • Kang, D., et al., 2020. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction, 118, 103291. doi:10.1016/j.autcon.2020.103291.
  • 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. doi:10.1016/j.conbuildmat.2021.126162.
  • Liu, J., et al., 2020. Automated pavement crack detection and segmentation based on two-step convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 35 (11), 1291–1305. doi:10.1111/mice.12622.
  • Mei, Q., and Gül, M., 2020. A cost effective solution for pavement crack inspection using cameras and deep neural networks. Construction and Building Materials, 256, 119397. doi:10.1016/j.conbuildmat.2020.119397.
  • Milletari, F., Navab, N., and Ahmadi, S.-A., 2016. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, 565–571.
  • Ni, F., Zhang, J., and Chen, Z., 2019. Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Computer-Aided Civil and Infrastructure Engineering, 34 (5), 367–384. doi:10.1111/mice.12421.
  • Oliveira, H., and Correia, P.L., 2009. Automatic road crack segmentation using entropy and image dynamic thresholding. In: 2009 17th European Signal Processing Conference, IEEE, 622–626.
  • Polovnikov, V., et al., 2021. DAUNet: deep augmented neural network for pavement crack segmentation. IEEE Access, 9, 125714–125723. doi:10.1109/ACCESS.2021.3111223.
  • Qiao, W., et al., 2021. Automatic pixel-level pavement crack recognition using a deep feature aggregation segmentation network with a scse attention mechanism module. Sensors, 21 (9), 2902. doi:10.3390/s21092902.
  • Ruder, S., 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. https://api.semanticscholar.org/CorpusID:17485266.
  • Shi, Y., et al., 2016. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17 (12), 3434–3445. doi:10.1109/TITS.2016.2552248.
  • Simonyan, K., and Zisserman, A., 2014. Two-Stream convolutional networks for action recognition in videos. Advances in Neural Information Processing Systems, 27, 568–576.
  • Song, W., et al., 2020. Automated pavement crack damage detection using deep multiscale convolutional features. Journal of Advanced Transportation, 2020, 1–11. doi:10.1155/2020/6412562.
  • Su, B., et al., 2022. FSRDD: an efficient few-shot detector for rare city road damage detection. IEEE Transactions on Intelligent Transportation Systems, 23 (12), 24379–24388. doi:10.1109/TITS.2022.3208188.
  • Sun, J., et al., 2023. Bi-Unet: a dual stream network for real-time highway surface segmentation. IEEE Transactions on Intelligent Vehicles, 8 (2), 1549–1563. doi:10.1109/TIV.2022.3216734.
  • Wang, W., Zhang, X., and Hong, H., 2015. Pavement crack detection combining non-negative feature with fast LoG in complex scene. In: MIPPR 2015: Automatic target recognition and navigation. vol. 9812 SPIE, 140–145.
  • Xiang, C., et al., 2023. A crack-segmentation algorithm fusing transformers and convolutional neural networks for complex detection scenarios. Automation in Construction, 152, 104894. doi:10.1016/j.autcon.2023.104894.
  • Xiao, Y., Cao, Z., and Yuan, J., 2014. Entropic image thresholding based on GLGM histogram. Pattern Recognition Letters, 40, 47–55. doi:10.1016/j.patrec.2013.12.017.
  • Xu, B., and Liu, C., 2022. Pavement crack detection algorithm based on generative adversarial network and convolutional neural network under small samples. Measurement, 196, 111219. doi:10.1016/j.measurement.2022.111219.
  • Xu, Y., et al., 2018. Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images. Structural Control and Health Monitoring, 25 (2), e2075. doi:10.1002/stc.2075.
  • 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. doi:10.1111/mice.12412.
  • Yong, H., and Chun-Xia, Z., 2010. A novel LBP based methods for pavement crack detection. Journal of Pattern Recognition Research, 5 (1), 140–147.
  • Zhao, H., Qin, G., and Wang, X., 2010. Improvement of canny algorithm based on pavement edge detection. In: 2010 3rd international congress on image and signal processing, IEEE, p. 964–967.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.