Show source-provided references
1. Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems
10.1016/j.ejor.2020.05.050 ↗3. Oliveira, H., and Correia, P.L. (2009, January 24–28). Automatic road crack segmentation using entropy and image dynamic thresholding. Proceedings of the 2009 17th European Signal Processing Conference (EUSIPCO), Glasgow, UK.
6. An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction
10.1155/2018/7419058 ↗8. CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection
10.1002/stc.2551 ↗9. Research progress of surface defect detection methods based on machine vision
10. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany.
10.1007/978-3-319-24574-4_28 ↗11. Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023, January 2–6). Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France.
10.1109/ICCV51070.2023.00558 ↗12. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Gemany.
10.1007/978-3-030-01234-2_1 ↗13. Deep learning-based crack damage detection using convolutional neural networks
10.1111/mice.12263 ↗16. Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection
10.1016/j.autcon.2020.103171 ↗17. Fan, R., Bocus, M.J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., and Liu, M. (2019, January 9–12). Road crack detection using deep convolutional neural network and adaptive thresholding. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.
10.1109/IVS.2019.8814000 ↗18. Xu, H., Su, X., Wang, Y., Cai, H., Cui, K., and Chen, X. (2019). Automatic bridge crack detection using a convolutional neural network. Appl. Sci., 9.
10.3390/app9142867 ↗21. Du, F.J., and Jiao, S.J. (2022). Improvement of lightweight convolutional neural network model based on YOLO algorithm and its research in pavement defect detection. Sensors, 22.
10.3390/s22093537 ↗22. Pavement crack detection and segmentation method based on improved deep learning fusion model
10.1155/2020/8515213 ↗23. Automated pavement crack detection and segmentation based on two-step convolutional neural network
10.1111/mice.12622 ↗24. Pixelwise asphalt concrete pavement crack detection via deep learning-based semantic segmentation method
10.1002/stc.2974 ↗25. CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework
10.1049/itr2.12173 ↗27. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22–29). Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision (ICCV), Venice, Italy.
10.1109/ICCV.2017.89 ↗28. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020, January 13–16). Dynamic convolution: Attention over convolution kernels. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.
10.1109/CVPR42600.2020.01104 ↗29. Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25–28). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.
10.1109/ICIP.2016.7533052 ↗32. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
10.1109/TPAMI.2017.2699184 ↗