(Sichaun University of Science & Engineering, China)(2) * Cao Li-jia
(Sichuan University of Science & Engineering, China)*corresponding author
AbstractWith the strong support of the country for bridge construction and the increase in supervision of the safety of old bridges, the visual-based bridge crack target detection has a problem of incomplete target framing due to the characteristics of the bridge crack target, reflecting the current algorithm model's poor ability to accurately identify targets. In this paper, YOLO V5 algorithm was used to address the issue of poor accuracy in bridge crack target detection, and a relevant bridge crack detection dataset was created. Three attention mechanisms, SENet, ECALayer, and CBAM, were respectively fused to improve the model's feature fusion part, and comparative experiments were conducted. The experimental results show that the improved algorithm has increased from 80.5% to 87% in mAP50-95 indicators compared to the original algorithm.
KeywordsObject detection；Bridge Crack Detection；Attention Mechanism；YOLO V5
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DOIhttps://doi.org/10.31763/ijrcs.v3i2.929 |
Article metrics10.31763/ijrcs.v3i2.929 Abstract views : 1085 | PDF views : 269 |
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References
[1] A. Ammouche, D. Breysse, H. Hornain, O. Didry, and J. Marchand, "A New Image Analysis Technique for the Quantitative Assessment of Microcracks in Cement-Based Materials," Cement and Concrete Research, vol. 30, pp. 25-35, 2000, https://doi.org/10.1016/S0008-8846(99)00212-4.
[2] H. D. Cheng, J. Chen, C. Glazier, and Y. G. Hu, "Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory," Journal of Computing in Civil Engineering, vol. 13, pp. 270-280, 1999, https://doi.org/10.1061/(ASCE)0887-3801(1999)13:4(270).
[3] M. Win, A. R. Bushroa, M. A. Hassan, N. M. Hilman, and A. Ide-Ektessabi, "A Contrast Adjustment Thresholding Method for Surface Defect Detection Based on Mesoscopy," IEEE Transactions on Industrial Informatics, vol. 11, pp. 642-649, 2015, https://doi.org/10.1109/TII.2015.2417676.
[4] H. Zhao, G. Qin and X. Wang, "Improvement of Canny Algorithm Based on Pavement Edge Detection," in 2010 3rd International Congress on Image and Signal Processing. vol. 2, pp. 964-967, 2010, https://doi.org/10.1109/CISP.2010.5646923.
[5] Z. Zhang, F. Xing, X. Shi, and L. Yang, "SemiContour: A Semi-Supervised Learning Approach for Contour Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 251-259, 2016, https://doi.org/10.1109/CVPR.2016.34.
[6] A. Sironi, E. Türetken, V. Lepetit, and P. Fua, "Multiscale Centerline Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1327-1341, 2016, https://doi.org/10.1109/TPAMI.2015.2462363.
[7] M. Kamaliardakani, L. Sun and M. K. Ardakani, "Sealed-Crack Detection Algorithm Using Heuristic Thresholding Approach," Journal of Computing in Civil Engineering, vol. 30, pp. 1943-5487, 2016, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000447.
[8] O. Henrique and C. P. Lobato, "Automatic Road Crack Detection and Characterization," IEEE Transactions on Intelligent Transportation Systems, vol. 14, pp. 155-168, 2013, https://doi.org/10.1109/TITS.2012.2208630.
[9] E. Zalama, J. G. García-Bermejo, R. Medina, and J. L. Fernández, "Road Crack Detection Using Visual Features Extracted by Gabor Filters," Computer Aided Civil and Infrastructure Engineering, vol. 29, pp. 342-358, 2014, https://doi.org/10.1111/mice.12042.
[10] R. Lienhart and J. Maydt, "An Extended Set of Haar-Like Features for Rapid Object Detection," in Proceedings. International Conference on Image Processing. vol. 1, pp. I-I, 2002, https://doi.org/10.1109/ICIP.2002.1038171.
[11] P. Prasanna, K. J. Dana, N. Gucunski, B. B. Basily, H. M. La, R. S. Lim, and H. Parvardeh, "Automated Crack Detection on Concrete Bridges," IEEE Transactions on Automation Science and Engineering, vol. 13, pp. 591-599, 2016, https://doi.org/10.1109/TASE.2014.2354314.
[12] A. Cord and S. Chambon, "Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost," Computer-Aided Civil and Infrastructure Engineering, vol. 27, pp. 244-259, 2012, https://doi.org/10.1111/j.1467-8667.2011.00736.x.
[13] Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, "Automatic Road Crack Detection Using Random Structured Forests," IEEE Transactions on Intelligent Transportation Systems, vol. 17, pp. 3434-3445, 2016, https://doi.org/10.1109/TITS.2016.2552248.
[14] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014, https://doi.org/10.1109/CVPR.2014.81,
[15] R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, 2015, https://doi.org/10.1109/ICCV.2015.169.
[16] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1137-1149, 2017, https://doi.org/10.1109/TPAMI.2016.2577031.
[17] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016, https://doi.org/10.1109/CVPR.2016.91.
[18] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," In Computer Vision–ECCV 2016: 14th European Conference, pp. 21-37, 2016, https://doi.org/10.1007/978-3-319-46448-0_2.
[19] L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, "Road Crack Detection Using Deep Convolutional Neural Network," in 2016 IEEE International Conference on Image Processing (ICIP), pp. 3708-3712, 2016, https://doi.org/10.1109/ICIP.2016.7533052.
[20] Q. Zou, Z. Zhang, Q. Li, X. Qi, Q. Wang, and S. Wang, "DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection," IEEE Transactions on Image Processing, vol. 28, pp. 1498-1512, 2019, https://doi.org/10.1109/TIP.2018.2878966.
[21] P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, "Understanding Convolution for Semantic Segmentation," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451-1460, 2018. https://doi.org/10.1109/WACV.2018.00163.
[22] S. Teng, Z. Liu and X. Li, "Improved YOLOv3-Based Bridge Surface Defect Detection by Combining High- and Low-Resolution Feature Images," Buildings, vol. 12, 2022, https://doi.org/10.3390/buildings12081225.
[23] J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv e-prints, pp., 2018, https://doi.org/10.48550/arXiv.1804.02767.
[24] J. S. Zhu and J. B. Song, "An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges," Applied Sciences-Basel, vol. 10, no. 3, p. 972, 2020, https://doi.org/10.3390/app10030972.
[25] P. Huthwohl, R. Lu and I. Brilakis, "Multi-Classifier for Reinforced Concrete Bridge Defects," Automation In Construction, vol. 105, 2019, https://doi.org/10.1016/j.autcon.2019.04.019.
[26] S. Zhao, F. Kang and J. Li, "Concrete Dam Damage Detection and Localisation Based on YOLOv5s-HSC and Photogrammetric 3D Reconstruction," Automation in Construction, vol. 143, 2022, https://doi.org/10.1016/j.autcon.2022.104555.
[27] G. Li, Q. W. Liu, S. M. Zhao, W. T. Qiao, and X. L. Ren, "Automatic Crack Recognition for Concrete Bridges Using a Fully Convolutional Neural Network and Naive Bayes Data Fusion Based on a Visual Detection System," Measurement Science and Technology, vol. 31, 2020, https://doi.org/10.1088/1361-6501/ab79c8.
[28] J. Hu, L. Shen and G. Sun, "Squeeze-and-Excitation Networks," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Salt, pp. 7132-7141, 2018, https://doi.org/10.1109/CVPR.2018.00745.
[29] W. Qilong, W. Banggu, Z. Pengfei, L. Peihua, Z. Wangmeng, and H. Qinghua, "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531-11539, 2020, https://openaccess.thecvf.com.
[30] S. Woo, J. Park, J. Lee, and I. S. Kweon, "CBAM: Convolutional Block Attention Module," In Proceedings of the European conference on computer vision (ECCV), pp. 3-19. 2018, https://doi.org/10.1007/978-3-030-01234-2_1.
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