2023, 15(4): 14-21. doi: 10.16670/j.cnki.cn11-5823/tu.2023.04.03
基于计算机视觉的混凝土缺陷检测研究综述
大连理工大学 建设管理系，大连 116024 |
A Review of Concrete Defect Detection Based on Computer Vision
Dalian University of Technology, Department of Construction Management, Dalian 116024, China |
引用本文:
姜韶华, 蒋希晗. 基于计算机视觉的混凝土缺陷检测研究综述[J]. 土木建筑工程信息技术,
2023, 15(4): 14-21.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.04.03
Citation:
Shaohua Jiang, Xihan Jiang. A Review of Concrete Defect Detection Based on Computer Vision[J]. Journal of Information Technologyin Civil Engineering and Architecture,
2023, 15(4): 14-21.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.04.03
摘要:混凝土缺陷对混凝土结构的安全性和稳定性造成的威胁不容小觑，因此，定期的缺陷检测对混凝土结构的维护至关重要。相较于主观低效的人工视觉检测，计算机视觉因在混凝土缺陷检测的自动化方面具有显著优势而成为近年来的研究热点，但目前缺乏该领域的全面综述。因此，本文旨在综合分析计算机视觉技术在混凝土缺陷检测领域的研究进展，对混凝土缺陷检测涉及的计算机视觉算法进行分类，总结现存的技术难点并分析未来研究方向，为该领域的后续研究提供一定的参考。
Abstract: Concrete defects pose a considerable threat to the safety and stability of concrete structures, so regular defect detection is essential for the maintenance of concrete structures. Compared with subjective and inefficient manual visual inspection, computer vision enjoys significant advantages in the automation of concrete defect detection, which has become a research hot spot in recent years. However, there is still a lack of comprehensive review in this field currently. Therefore, this paper aims to comprehensively analyze the research progress of computer vision technique in the field of concrete defect detection and classify the computer vision algorithms involved in concrete defect detection. Thereafter the paper summarizes the existing technical difficulties and analyzes the future research directions, which can provide a certain reference for the subsequent research in this field.
| [1] |
温作林. 基于深度学习的混凝土裂缝识别[D]. 杭州: 浙江大学, 2019. |
| [2] |
李若星. 基于机器视觉的混凝土裂缝检测方法研究[D]. 重庆: 重庆大学, 2018. |
| [3] |
蒋燕芳. 基于图像处理与深度学习的RC桥梁表观病害识别[D]. 重庆: 重庆大学, 2019. |
| [4] |
McLaughlin E, Charron N, Narasimhan S. Automated defect quantification in concrete bridges using robotics and deep learning[J]. Journal of Computing in Civil Engineering, 2020, 34 (5). |
| [5] |
Ju H Y, Li W, Tighe S S, et al. CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection[J]. Structural Control & Health Monitoring, 2020, 27 (8): 19. |
| [6] |
Huang H W, Li Q T, Zhang D M. Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology, 2018, 77: 166-176.doi: 10.1016/j.tust.2018.04.002 |
| [7] |
Koch C, Georgieva K, Kasireddy V, et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure[J]. Advanced Engineering Informatics, 2015, 29 (2): 196-210.doi: 10.1016/j.aei.2015.01.008 |
| [8] |
Hu W, Wang W, Ai C, et al. Machine vision-based surface crack analysis for transportation infrastructure[J]. Automation in Construction, 2021, 132. |
| [9] |
Andrushia A D, Anand N, Neebha T M, et al. Autonomous detection of concrete damage under fire conditions[J]. Automation in Construction, 2022, 140. |
| [10] |
Chow J K, Su Z, Wu J, et al. Artificial intelligence-empowered pipeline for image-based inspection of concrete structures[J]. Automation in Construction, 2020, 120. |
| [11] |
Yang Q N, Shi W M, Chen J, et al. Deep convolution neural network-based transfer learning method for civil infrastructure crack detection[J]. Automation in Construction, 2020, 116: 9. |
| [12] |
Huthwohl P, Lu R D, Brilakis I. Multi-classifier for reinforced concrete bridge defects[J]. Automation in Construction, 2019, 105: 15. |
| [13] |
Zhou S L, Song W. Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection[J]. Automation in Construction, 2020, 114: 17. |
| [14] |
Feng C C, Zhang H, Wang S, et al. Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning[J]. Ksce Journal of Civil Engineering, 2019, 23 (10): 4493-4502.doi: 10.1007/s12205-019-0437-z |
| [15] |
Bhattacharya G, Mandal B, Puhan N B. Multi-deformation aware attention learning for concrete structural defect classification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31 (9): 3707-3713.doi: 10.1109/TCSVT.2020.3028008 |
| [16] |
Cha Y J, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32 (5): 361-378.doi: 10.1111/mice.12263 |
| [17] |
Liu Z, Cao Y, Wang Y, et al. Computer vision-based concrete crack detection using U-net fully convolutional networks[J]. Automation in Construction, 2019, 104: 129-139.doi: 10.1016/j.autcon.2019.04.005 |
| [18] |
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014: 580-587. |
| [19] |
Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 779-788. |
| [20] |
Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//14th European Conference on Computer Vision, ECCV 2016. 2016: 21-37. |
| [21] |
Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. 2015: 1440-1448. |
| [22] |
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137-1149.doi: 10.1109/TPAMI.2016.2577031 |
| [23] |
Kang D, Benipal S S, Gopal D L, et al. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning[J]. Automation in Construction, 2020, 118. |
| [24] |
Beckman G H, Polyzois D, Cha Y J. Deep learning-based automatic volumetric damage quantification using depth camera[J]. Automation in Construction, 2019, 99: 114-124.doi: 10.1016/j.autcon.2018.12.006 |
| [25] |
Zhao S, Kang F, Li J. Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction[J]. Automation in Construction, 2022, 143. |
| [26] |
Zhang C B, Chang C C, Jamshidi M. Concrete bridge surface damage detection using a single-stage detector[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35 (4): 389-409.doi: 10.1111/mice.12500 |
| [27] |
Yu Z W, Shen Y G, Shen C K. A real-time detection approach for bridge cracks based on YOLOv4-FPM[J]. Automation in Construction, 2021, 122: 11. |
| [28] |
Ma G, Wu M, Wu Z, et al. Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects[J]. Journal of Building Engineering, 2021, 38. |
| [29] |
Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440. |
| [30] |
Han X, Zhao Z, Chen L, et al. Structural damage-causing concrete cracking detection based on a deep-learning method[J]. Construction and Building Materials, 2022, 337. |
| [31] |
Liu Y, Yeoh J K W. Robust pixel-wise concrete crack segmentation and properties retrieval using image patches[J]. Automation in Construction, 2021, 123. |
| [32] |
Mei Q P, Gul M, Azim M R. Densely connected deep neural network considering connectivity of pixels for automatic crack detection[J]. Automation in Construction, 2020, 110: 13. |
| [33] |
Deng W, Mou Y, Kashiwa T, et al. Vision based pixel-level bridge structural damage detection using a link ASPP network[J]. Automation in Construction, 2020, 110. |
| [34] |
Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (12): 2481-2495.doi: 10.1109/TPAMI.2016.2644615 |
| [35] |
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. 2015: 234-241. |
| [36] |
Chen J, Lu W, Lou J. Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic-rich building information model[J]. Computer-Aided Civil and Infrastructure Engineering, 2022. |
| [37] |
Jang K, An Y K, Kim B, et al. Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot[J]. Computer-Aided Civil and Infrastructure Engineering, 2021, 36 (1): 14-29.doi: 10.1111/mice.12550 |
| [38] |
Ren Y, Huang J, Hong Z, et al. Image-based concrete crack detection in tunnels using deep fully convolutional networks[J]. Construction and Building Materials, 2020, 234. |
| [39] |
Ali R, Cha Y J. Attention-based generative adversarial network with internal damage segmentation using thermography[J]. Automation in Construction, 2022, 141. |
| [40] |
Asadi Shamsabadi E, Xu C, Rao A S, et al. Vision transformer-based autonomous crack detection on asphalt and concrete surfaces[J]. Automation in Construction, 2022, 140. |
| [41] |
Chaiyasarn K, Buatik A, Mohamad H, et al. Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures[J]. Automation in Construction, 2022, 140. |
| [42] |
Li S, Zhao X, Zhou G. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network[J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34 (7): 616-634.doi: 10.1111/mice.12433 |
| [43] |
He K, Gkioxari G, Dollar P, et al. Mask R-CNN[C]//16th IEEE International Conference on Computer Vision, ICCV 2017. 2017: 2980-2988. |
| [44] |
Wei F, Yao G, Yang Y, et al. Instance-level recognition and quantification for concrete surface bughole based on deep learning[J]. Automation in Construction, 2019, 107. |
| [45] |
Kirillov A, Wu Y, He K, et al. Pointrend: Image segmentation as rendering[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2020: 9796-9805. |
| [46] |
Liu Y, Yeoh J K W, Chua D K H. Deep learning-based enhancement of motion blurred UAV concrete crack images[J]. Journal of Computing in Civil Engineering, 2020, 34 (5). |
| [47] |
Li G, Zhao X, Du K, et al. Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine[J]. Automation in Construction, 2017, 78: 51-61.doi: 10.1016/j.autcon.2017.01.019 |
| [48] |
Deng J, Lu Y, Lee V C S. Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35 (4): 373-388.doi: 10.1111/mice.12497 |
计量
- PDF下载量(88)
- 文章访问量(2950)
- HTML全文浏览量(825)
