Road surface crack detection based on improved YOLOv5s

Jiaming Ding; Peigang Jiao; Kangning Li; Weibo Du; Jiaming Ding; Peigang Jiao; Kangning Li; Weibo Du

doi:10.3934/mbe.2024188

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4269-4285. doi: 10.3934/mbe.2024188

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Research article Special Issues

Road surface crack detection based on improved YOLOv5s

Shandong Jiaotong University, Jinan 250357, China

Academic Editor: Namhyuk Ahn

Received: 23 November 2023 Revised: 17 January 2024 Accepted: 29 January 2024 Published: 26 February 2024

In response to the issues of low efficiency and high cost in traditional manual methods for road surface crack detection, an improved YOLOv5s (you only look once version 5 small) algorithm was proposed. Based on this improvement, a road surface crack object recognition model was established using YOLOv5s. First, based on the Res2Net (a new multi-scale backbone architecture) network, an improved multi-scale Res2-C3 (a new multi-scale backbone architecture of C3) module was suggested to enhance feature extraction performance. Second, the feature fusion network and backbone of YOLOv5 were merged with the GAM (global attention mechanism) attention mechanism, reducing information dispersion and enhancing the interaction of global dimensions features. We incorporated dynamic snake convolution into the feature fusion network section to enhance the model's ability to handle irregular shapes and deformation problems. Experimental results showed that the final revision of the model dramatically increased both the detection speed and the accuracy of road surface identification. The mean average precision (mAP) reached 93.9%, with an average precision improvement of 12.6% compared to the YOLOv5s model. The frames per second (FPS) value was 49.97. The difficulties of low accuracy and slow speed in road surface fracture identification were effectively addressed by the modified model, demonstrating that the enhanced model achieved relatively high accuracy while maintaining inference speed.
- road surface crack detection,
- deep learning,
- YOLOv5s,
- Res2-C3 module,
- attention mechanism
Citation: Jiaming Ding, Peigang Jiao, Kangning Li, Weibo Du. Road surface crack detection based on improved YOLOv5s[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4269-4285. doi: 10.3934/mbe.2024188

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Abstract

In response to the issues of low efficiency and high cost in traditional manual methods for road surface crack detection, an improved YOLOv5s (you only look once version 5 small) algorithm was proposed. Based on this improvement, a road surface crack object recognition model was established using YOLOv5s. First, based on the Res2Net (a new multi-scale backbone architecture) network, an improved multi-scale Res2-C3 (a new multi-scale backbone architecture of C3) module was suggested to enhance feature extraction performance. Second, the feature fusion network and backbone of YOLOv5 were merged with the GAM (global attention mechanism) attention mechanism, reducing information dispersion and enhancing the interaction of global dimensions features. We incorporated dynamic snake convolution into the feature fusion network section to enhance the model's ability to handle irregular shapes and deformation problems. Experimental results showed that the final revision of the model dramatically increased both the detection speed and the accuracy of road surface identification. The mean average precision (mAP) reached 93.9%, with an average precision improvement of 12.6% compared to the YOLOv5s model. The frames per second (FPS) value was 49.97. The difficulties of low accuracy and slow speed in road surface fracture identification were effectively addressed by the modified model, demonstrating that the enhanced model achieved relatively high accuracy while maintaining inference speed.

References

[1]	C..P. Meng, J. P. Li, J. Guo, C. L. Li, Analysis of common problems in ecological impact investigation of highway environmental protection acceptance, Res. Cons. Envir. Prot., 4 (2023), 121–124. https://doi.org/10.16317/j.cnki.12-1377/x.2023.04.015 doi: 10.16317/j.cnki.12-1377/x.2023.04.015
[2]	W. Zhou, Y. He, J. Li, Dangerous behavior detection in gas stations based on deep learning, in 2023 IEEE 6th International Conference on Electronic Information and Communication Technology, (2023), 935–939. https://doi.org/10.1109/ICEICT57916.2023.10245093
[3]	N. Sholevar, A. Golroo, S. R. Esfahani, Machine learning techniques for pavement condition evaluation, Autom. Constr., 136 (2022), 104190. https://doi.org/10.1016/j.autcon.2022.104190 doi: 10.1016/j.autcon.2022.104190
[4]	H. Bello-Salau, A. M. Aibinu, E. N. Onwuka, J. J. Dukiya, A. J. Onumanyi, Image processing techniques for automated road defect detection: A survey, in International Conference on Electronics, (2014), 1–4. https://doi.org/10.1109/ICECCO.2014.6997556
[5]	S. Chatterjee, P. Saeedfar, S. Tofangchi, L. M. Kolbe, Intelligent road maintenance: a machine learning approach for surface defect detection, in European Conference on Information Systems, 2018.
[6]	J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 779–788.
[7]	S. Park, S. Bang, H. Kim, H. Kim, Patch-based crackdetection in black box images using convolutional neural net-works, J. Comput. Civil Eng., 33 (2019). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000831 doi: 10.1061/(ASCE)CP.1943-5487.0000831
[8]	X. B. Su, Research on pavement crack detection based on improved YOLOv4, Henan Sci. Technol., 41 (2022), 62–67. https://doi.org/10.19968/j.cnki.hnkj.1003-5168.2022.18.012 doi: 10.19968/j.cnki.hnkj.1003-5168.2022.18.012
[9]	M. M. Wang, Q. D. Huang, S. N. Liu, Pavement damage detection based on improved YOLOv5s, J. Lasers, 44 (2023), 66–71. https://doi.org/10.14016/j.cnki.jgzz.2023.05.066 doi: 10.14016/j.cnki.jgzz.2023.05.066
[10]	J. Terven, D. Cordova-Esparza, A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond, preprint, arXiv: 2304.00501.
[11]	J. Lu, M. Zhu, X. Ma, K. Wu, Steel strip surface defect detection Metho based on improved YOLOv5s, Biomimetics, 9 (2024), 28. https://doi.org/10.3390/biomimetics9010028 doi: 10.3390/biomimetics9010028
[12]	Y. Zhou, W. Zhu, Y. He, Y. Li, Yolov8-based spatial target part recognition, in 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence, (2023), 1684–1687. https://doi.org/10.1109/ICIBA56860.2023.10165260
[13]	H. Liu, F. Sun, J. Gu, L. Deng, Sf-yolov5: A lightweight small object detection algorithm based on improved feature fusion mode, Sensors, 22 (2022), 5817. https://doi.org/10.3390/s22155817 doi: 10.3390/s22155817
[14]	J. Zhou, Z. Xi, S. Wang, B. Yang, Y. Zhang, Y. Zhang, A real spatial–temporal attention denoising network for nugget quality detection in resistance spot weld, J. Intell. Manuf., 2023 (2023), 1–22. https://doi.org/10.1007/s10845-023-02160-x doi: 10.1007/s10845-023-02160-x
[15]	C. Y. Wang, H. Y. M. Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, IH Yeh CSPNet: A new backbone that can enhance learning capability of CNN, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, (2020), 390–391.
[16]	K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint, arXiv: 1409.1556.
[17]	C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, in Proceedings of the IEEE conference on computer vision and pattern recognition, (2015), 1–9.
[18]	X. Jiang, H. Hu, Y. Qin, Y. Hu, R. Ding, A real-time rural domestic garbage detection algorithm with an improved YOLOv5s network model, Sci. Rep., 12 (2022), 16802. https://doi.org/10.1038/s41598-022-20983-1 doi: 10.1038/s41598-022-20983-1
[19]	S. H. Gao, M. M. Cheng, K. Zhao, X. Y. Zhang, M. H. Yang, P. Torr, Res2Net: A New Multi-Scale Backbone Architecture, IEEE Trans. Pattern Anal. Mach. Intell., 2 (2021), 43. https://doi.org/10.1109/TPAMI.2019.2938758 doi: 10.1109/TPAMI.2019.2938758
[20]	U. Batool, M. I. Shapiai, S. A. Mostafa, M. Z. Ibrahim, An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification, IEEE Access, 11 (2023), 108891–108905. https://doi.org/10.1109/ACCESS.2023.3321025 doi: 10.1109/ACCESS.2023.3321025
[21]	Z. Yu, H. Huang, W. Chen, Y. Su, Y. Liu, X. Wang, Yolo-facev2: A scale and occlusion aware face detector, preprint, arXiv: 2208.02019.
[22]	J. Cai, J. Hu, 3D RANs: 3D residual attention networks for action recognition, Vis. Comput., 36 (2020), 1261–1270. https://doi.org/10.1007/s00371-019-01733-3 doi: 10.1007/s00371-019-01733-3
[23]	J. Hu, L. Shen, G. Sun, Squeeze-and-Excitation networks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
[24]	S. Woo, J. Park, J. Y. Lee, I. S. Kweon, Cbam: Convolutional block attention module, in Proceedings of the European conference on computer vision (ECCV), (2018), 3–19.
[25]	Y. Liu, Z. Shao, N. Hoffmann, Global attention mechanism: Retain information to enhance channel-spatial interactions, preprint, arXiv: 2112.05561.
[26]	Z. Guo, Y. Li, Y. Tian, H. Liu, S. Yuan, C. Hou, Global attention-based approach for substation devices classification and localization, in 2023 IEEE/IAS Industrial and Commercial Power System Asia, (2023), 990–995. https://doi.org/10.1109/ICPSAsia58343.2023.10294513
[27]	Y. Qi, Y. He, X. Qi, Y. Zhang, G. Yang, Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation, in Proceedings of the IEEE/CVF International Conference on Computer Vision, (2023), 6070–6079. https://doi.org/10.1109/ICCV51070.2023.00558
[28]	J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, et al., Deformable convolutional networks, in Proceedings of the IEEE international conference on computer vision, (2017), 764–773. https://doi.org/10.1109/ICCV.2017.89
[29]	Z. Wu, R. Xue, H. Li, Real-time video fire detection via modified YOLOv5 network model, Fire Technol., 58 (2022), 2377–2403. https://doi.org/10.1007/s10694-022-01260-z doi: 10.1007/s10694-022-01260-z
[30]	Y. Wang, G. Fu, A novel object recognition algorithm based on improved YOLOv5 model for patient care robots, Int. J. Hum. Robot., 19 (2022). https://doi.org/10.1142/S0219843622500104 doi: 10.1142/S0219843622500104
[31]	L. Shi, S. Zhao, W. Niu, A welding defect detection method based on multiscale feature enhancement and aggregation, Nond. Testing and Eval., (2023), 1–20. https://doi.org/10.1080/10589759.2023.2253494 doi: 10.1080/10589759.2023.2253494
[32]	E. R. Daniel, Wildfire smoke detection with computer vision, preprint, arXiv: 2301.05070.

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