Multiple distresses detection for Asphalt Pavement using improved you Only Look Once Algorithm based on convolutional neural network

References

• Barbu, T., 2013. Variational image denoising approach with diffusion porous media flow. In: Proc., Abstract and Applied Analysis, 1–8. doi:10.1155/2013/856876.
   Google Scholar
• Bhutani, K.R., and Battou, A., 1995. An application of fuzzy relations to data augmentation. Pattern Recognition Letters, 16 (9), 901–909. doi:10.1016/0167-8655(95)00035-F.
   Web of Science ®Google Scholar
• Bochkovskiy, A., Wang, C.-Y., and Liao, H., 2020. Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 1–17. doi:10.48550/arXiv.2004.10934.
   Google Scholar
• Cheng, H.D., Chen, J.R., and Glazier, C., 1996. Novel fuzzy logic approach to pavement distress detection. In: Nondestructive evaluation of bridges & highways, 97–108. doi:10.1117/12.259128
   Google Scholar
• Chollet, F., 2017. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258. doi:10.1109/CVPR.2017.195.
   Google Scholar
• Dai, J., et al., 2016. R-fcn: Object detection via region-based fully convolutional networks. Advances in Neural Information Processing Systems, 29, 1–11. doi:10.48550/arXiv.1605.06409.
   Google Scholar
• Ding, X., et al., 2021. Repvgg: Making vgg-style convnets great again. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13733–13742. doi:10.1109/CVPR46437.2021.01352.
   Google Scholar
• Ding, X., et al., 2022. Re-parameterizing your optimizers rather than architectures. arXiv preprint arXiv:2205.15242, 1–18. doi:10.48550/arXiv.2205.15242.
   Google Scholar
• Dong, H., et al., 2021. Deep metric learning-based for multi-target few-shot pavement distress classification. IEEE Transactions on Industrial Informatics, 18 (3), 1801–1810. doi:10.1109/TII.2021.3090036.
   Web of Science ®Google Scholar
• Du, Y., et al., 2021. Pavement distress detection and classification based on YOLO network. International Journal of Pavement Engineering, 22 (13), 1659–1672. doi:10.1080/10298436.2020.1714047.
   Web of Science ®Google Scholar
• Everingham, M., et al., 2010. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 303–338. doi:10.1007/s11263-009-0275-4.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Feng, X., Shen, Y., and Wang, D., 2021. A survey on the development of image data augmentation. Computer Science Application, 11 (2), 370–382. doi:10.12677/CSA.2021.112037.
   Google Scholar
• Girshick, R., 2015. Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, 1440–1448. doi:10.1109/ICCV.2015.169.
   Google Scholar
• Gonzalez, R.C., Woods, R.E., and Masters, B.R., 2009. Digital image processing, third edition. Journal of Biomedical Optics, 14 (2), 029901. doi:10.1117/1.3115362.
   Google Scholar
• Gopalakrishnan, K., et al., 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330. doi:10.1016/j.conbuildmat.2017.09.110.
   PubMed Web of Science ®Google Scholar
• Guo, G., and Zhang, Z., 2022. Road damage detection algorithm for improved YOLOv5. Scientific Reports, 12 (1), 1–12. doi:10.1038/s41598-022-19674-8.
   PubMedGoogle Scholar
• He-Yong, W., et al., 2008. Research of imbalanced data classification. Application Research of Computers, 25 (5), 1301–1303. doi:1301-1303.10.1145/1360612.1360700.
   Google Scholar
• He, K., et al., 2017. Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, 2961–2969. doi:10.1109/TPAMI.2018.2844175.
   Google Scholar
• Hou, Q., Zhou, D., and Feng, J., 2021. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13713–13722. doi:10.1109/CVPR46437.2021.01350
   Google Scholar
• 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. doi:10.1002/stc.2551.
   Web of Science ®Google Scholar
• Jian, M., et al., 2017. Review of pavement detection technology. Journal of Traffic & Transportation Engineering, 17 (5), 121–137. doi:10.3969/j.issn.1671-1637.2017.05.012.
   Google Scholar
• Kingma, D.P., and Ba, J, 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 1–15. doi:10.48550/arXiv.1412.6980.
   Google Scholar
• Li, B., et al., 2020. Automatic classification of pavement crack using deep convolutional neural network. International Journal of Pavement Engineering, 21 (4), 457–463. doi:10.1080/10298436.2018.1485917.
   Web of Science ®Google Scholar
• Li, C., et al., 2022. YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976, 1–17. doi:10.48550/arXiv.2209.02976.
   Google Scholar
• Lin, T.-Y., et al., 2014. Microsoft coco: common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer, 740-755. doi:10.1007/978-3-319-10602-1_48.
   Google Scholar
• Lin, T.Y., et al., 2017. Focal loss for dense object detection. IEEE Transactions on Pattern analysis and Machine Intelligence, 42 (2), 318–327. doi:10.1109/TPAMI.2018.2858826.
   Google Scholar
• Liu, W., et al., 2016. Ssd: Single shot multibox detector. In: Computer vision–ECCV 2016: 14th european conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, Springer, 21–37. doi:10.1007/978-3-319-46448-0_2.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Liu, F., Liu, J., and Wang, L., 2022a. Asphalt pavement crack detection based on convolutional neural network and infrared thermography. IEEE Transactions on Intelligent Transportation Systems, 23 (11), 22145–22155. doi:10.1109/TITS.2022.3142393.
   Web of Science ®Google Scholar
• Liu, F., Liu, J., and Wang, L., 2022b. Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning. Automation in Construction, 143, 104575. doi:10.1016/j.autcon.2022.104575.
   Web of Science ®Google Scholar
• Liu, F., Liu, J., and Wang, L., 2022c. Deep learning and infrared thermography for asphalt pavement crack severity classification. Automation in Construction, 140, 104383. doi:10.1016/j.autcon.2022.104383.
   Web of Science ®Google Scholar
• Lyu, C., et al., 2022. RTMDet: An Empirical Study of Designing Real-Time Object Detectors. arXiv preprint arXiv:2212.07784, 1–15. doi:10.48550/arXiv.2212.07784.
   Google Scholar
• Mandal, V., Uong, L., and Adu-Gyamfi, Y., 2018. Automated Road crack detection using deep convolutional neural networks. In: 2018 IEEE international conference on big data (big data), IEEE, 5212–5215. doi:10.1109/BigData.2018.8622327.
   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. doi:10.1016/j.autcon.2018.07.008.
   Web of Science ®Google Scholar
• Redmon, J., and Farhadi, A., 2018. Yolov3: An incremental improvement. arXiv e-prints, 1–6. doi:10.48550/arXiv.1804.02767.
   Google Scholar
• Ren, S., et al., 2017. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39 (6), 1137–1149. doi:10.1109/TPAMI.2016.2577031.
   PubMed Web of Science ®Google Scholar
• Tan, L., et al., 2021. Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Medical Informatics and Decision Making, 21, 1–11. doi:10.1186/s12911-021-01691-8.
   PubMed Web of Science ®Google Scholar
• Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M., 2021. Scaled-yolov4: scaling cross stage partial network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13029–13038. doi:10.1109/CVPR46437.2021.01283
   Google Scholar
• 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. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7464–7475. doi:10.48550/arXiv.2207.02696
   Google Scholar
• Xu, S., et al., 2022a. PP-YOLOE: An evolved version of YOLO. arXiv preprint arXiv:2203.16250, 1–7. doi:10.48550/arXiv.2203.16250.
   Google Scholar
• Xu, X., et al., 2022b. Crack detection and comparison study based on faster R-CNN and mask R-CNN. Sensors, 22 (3), 1215. doi:10.3390/s22031215.
   PubMed 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. doi:10.1109/TITS.2019.2910595.
   Web of Science ®Google Scholar
• Yusof, N., et al., 2018. Crack detection and classification in asphalt pavement images using deep convolution neural network. In: 2018 8th IEEE international conference on control system, computing and engineering (ICCSCE), IEEE, 227–232. doi:10.1109/iccsce.2018.8685007.
   Google Scholar
• Zhang, D., et al., 2009. Automatic ridgelet data augmentation algorithm for road crack image based on fuzzy entropy and fuzzy divergence. Optics and Lasers in Engineering, 47, 1216–1225. doi:10.1016/J.OPTLASENG.2009.05.014.
   Web of Science ®Google Scholar
• Zuo, Y., Wang, G., and Zuo, C., 2008. Wavelet packet denoising for pavement surface cracks detection. In: 2008 International conference on computational intelligence and security. IEEE, 2, 481–484. doi:10.1109/CIS.2008.208.
   Google Scholar