417
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Asphalt pavement crack detection based on infrared thermography and deep learning

, , ORCID Icon, &
Article: 2295906 | Received 04 Aug 2023, Accepted 08 Dec 2023, Published online: 22 Dec 2023

References

  • Anon., 2022a. UTi-320E [online]. Available from: https://meters.uni-trend.com.cn/list_43/240.html.
  • Anon., 2022b. Why use SPPCSPC instead of SPPFCSPC [online]. Available from: https://github.com/WongKinYiu/yolov7/issues/658.
  • Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M., 2020. Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
  • Chen, L.J., et al., 2023. The classification and localization of crack using lightweight convolutional neural network with CBAM. Engineering Structures, 275, 115291. https://doi.org/10.1016/j.engstruct.2022.115291
  • China, N.B.o.S.o., 2023. China statistical yearbook 2022 [online]. Available from: http://www.stats.gov.cn/sj/ndsj/2022/indexch.htm.
  • Dang, L.M., et al., 2022. Deep learning-based masonry crack segmentation and real-life crack length measurement. Construction and Building Materials, 359, 129438. https://doi.org/10.1016/j.conbuildmat.2022.129438
  • Goodfellow, I., et al., 2020. Generative adversarial networks. Communications of the ACM, 63 (11), 139–144. https://doi.org/10.1145/3422622
  • Han, K., et al., 2020. Ghostnet: more features from cheap operations. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1577–1586. https://doi.org/10.1109/CVPR42600.2020.00165
  • Hoang, N.D., 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. https://doi.org/10.1016/j.autcon.2018.07.008
  • Li, X., et al., 2019. Selective Kernel networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 510–519. https://doi.org/10.1109/CVPR.2019.00060
  • Li, B.X., et al., 2020. Automatic classification of pavement crack using deep convolutional neural network. International Journal of Pavement Engineering, 21 (4), 457–463. https://doi.org/10.1080/10298436.2018.1485917
  • Li, C., et al., 2023. A domain adaptation YOLOv5 model for industrial defect inspection. Measurement, 213, 112725. https://doi.org/10.1016/j.measurement.2023.112725
  • Liu, W., et al., 2016. SSD: single shot multiBox detector. Computer Vision – ECCV 2016, PT I, 9905, 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
  • Liu, Y.H., et al., 2019. Deepcrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing, 338, 139–153. https://doi.org/10.1016/j.neucom.2019.01.036
  • Liu, S., Han, Y., and Xu, L., 2022c. Recognition of road cracks based on multi-scale Retinex fused with wavelet transform. Array, 15, 100193. https://doi.org/10.1016/j.array.2022.100193
  • Liu, F.Y., Liu, J., and Wang, L.B., 2022a. Asphalt pavement crack detection based on convolutional neural network and infrared thermography. Ieee Transactions on Intelligent Transportation Systems, 23 (11), 22145–22155. https://doi.org/10.1109/TITS.2022.3142393
  • Liu, F.Y., Liu, J., and Wang, L.B., 2022b. Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning. Automation in Construction, 143, 104575. https://doi.org/10.1016/j.autcon.2022.104575
  • Majidifard, H., Adu-Gyamfi, Y., and Buttlar, W.G., 2020. Deep machine learning approach to develop a new asphalt pavement condition index. Construction and Building Materials, 247. https://doi.org/10.1016/j.conbuildmat.2020.118513
  • Mohammadi Kazaj, P., 2021. yolov5-gradcam [online]. Available from: https://github.com/pooya-mohammadi/yolov5-gradcam.
  • Oksuz, K., et al., 2021. Imbalance problems in object detection: a review. Ieee Transactions on Pattern Analysis and Machine Intelligence, 43 (10), 3388–3415. https://doi.org/10.1109/TPAMI.2020.2981890
  • Ozkanoglu, M.A. and Ozer, S., 2022. InfraGAN: a GAN architecture to transfer visible images to infrared domain. Pattern Recognition Letters, 155, 69–76. https://doi.org/10.1016/j.patrec.2022.01.026
  • Park, T., et al., 2020b. Contrastive learning for unpaired image-to-image translation. ed. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX , 16, 319–345. https://doi.org/10.48550/arxiv.2007.15651
  • Park, S.E., Eem, S.H., and Jeon, H., 2020a. Concrete crack detection and quantification using deep learning and structured light. Construction and Building Materials, 252, 119096. https://doi.org/10.1016/j.conbuildmat.2020.119096
  • Que, Y., et al., 2023. Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model. Engineering Structures, 277, 115406. https://doi.org/10.1016/j.engstruct.2022.115406
  • Selvaraju, R.R., et al., 2020. Grad-CAM: visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128 (2), 336–359. https://doi.org/10.1007/s11263-019-01228-7
  • Sun, G.Q., et al., 2021. Chemo-rheological and morphology evolution of polymer modified bitumens under thermal oxidative and all-weather aging. Fuel, 285, 118989. https://doi.org/10.1016/j.fuel.2020.118989
  • Tzutalin, 2015. labelImg [online]. Available from: https://github.com/tzutalin/labelImg.
  • Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M., 2023. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. ed. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7464–7475.
  • Yan, Y.D., et al., 2022b. CycleADC-Net: a crack segmentation method based on multi-scale feature fusion. Measurement, 204, 112107. https://doi.org/10.1016/j.measurement.2022.112107
  • Yang, F., et al., 2020. Feature pyramid and hierarchical boosting network for pavement crack detection. Ieee Transactions on Intelligent Transportation Systems, 21 (4), 1525–1535. https://doi.org/10.1109/TITS.2019.2910595
  • Yang, G.D., et al., 2022b. Datasets and processing methods for boosting visual inspection of civil infrastructure: a comprehensive review and algorithm comparison for crack classification, segmentation, and detection. Construction and Building Materials, 356, 129226. https://doi.org/10.1016/j.conbuildmat.2022.129226
  • Yu, H.N., et al., 2021. Effect of ultraviolet aging on dynamic mechanical properties of SBS modified asphalt mortar. Construction and Building Materials, 281, 122328. https://doi.org/10.1016/j.conbuildmat.2021.122328
  • Zhang, Y.C., et al., 2022. Road damage detection using UAV images based on multi-level attention mechanism. Automation in Construction, 144. https://doi.org/10.1016/j.autcon.2022.104613
  • Zheng, Z.H., et al., 2020. Distance-IoU loss: faster and better learning for bounding box regression. Thirty-Fourth Aaai Conference on Artificial Intelligence, the Thirty-Second Innovative Applications of Artificial Intelligence Conference and the Tenth Aaai Symposium on Educational Advances in Artificial Intelligence, 34, 12993–13000.

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.