Segmentation of Bridge Components from Various Real Scene Inspection Images
Publication: Construction Research Congress 2024
ABSTRACT
The traditional bridge inspection method, which relies on manual visual inspection, is time-consuming, labor-intensive, and could be dangerous. Recent automated bridge inspection approaches aim to utilize unmanned aerial vehicles (UAVs) and computer vision techniques to collect and analyze images to improve the inspection process. A survey of existing literature and tools shows that defect detection/segmentation has been studied extensively. However, there has been little effort focused on segmenting and characterizing the bridge components that have the defects. The identification and characterization of bridge components is essential for bridge inspection, which can contextualize the defects to determine their importance in maintenance decision making. Moreover, existing bridge component recognition approaches lack generalizability in the presence of a variety of bridge types, complex background scenes, and varying shot sizes. To address these gaps, this paper proposes a convolutional neural network (CNN)-based image segmentation method to segment bridge components, which leverages DeepLabv3+ and pre-training from ImageNet to improve feature extraction and generalizability. The proposed method was trained and tested end to end on 13 classes based on the Federal Highway Administration (FHWA)’s Bridge Inspector’s Reference Manual. It achieved a mean precision, recall, F-1 measure, and Intersection over Union (IoU) of 86.7%, 78.2%, 81.4%, and 70.4%, respectively.
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REFERENCES
Baheti, B., Innani, S., Gajre, S., and Talbar, S. (2020). “Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment.” Proc., IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.
Bai, M., and Sezen, H. (2021). “Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks.” Proc., ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., XXIV ISPRS Congress, International Society for Photogrammetry and Remote Sensing.
Bianchi, E., and Hebdon, M. (2022). “Visual Structural Inspection Datasets.” Autom. Constr., 139, 104299.
Cha, Y.-J., Choi, W., and Büyüköztürk, O. (2017). “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks.” Comput.-Aided Civ. Infrastruct. Eng., 32(5), 361–378.
Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., and Büyüköztürk, O. (2018). “Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types.” Comput.-Aided Civ. Infrastruct. Eng., 33(9), 731–747.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2014). “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.”.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2017). “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Trans. Pattern Anal. Mach. Intell., 40(4), 834–848.
Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). “Rethinking atrous convolution for semantic image segmentation.”.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). “Encoder-decoder with atrous separable convolution for semantic image segmentation.” Proc., European conference on computer vision (ECCV).
MMSegmentation Contributors. (2020). “MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark.” from https://github.com/open-mmlab/mmsegmentation.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). “ImageNet: A large-scale hierarchical image database.” Proc., 2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.”.
Floridi, L., and Chiriatti, M. (2020). “GPT-3: Its Nature, Scope, Limits, and Consequences.” Minds and Machines, 30(4), 681–694.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). “Deep Residual Learning for Image Recognition.” Proc., IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.
Hartle, R. A., Ryan, T. W., Mann, E., Danovich, L. J., Sosko, W. B., and Bouscher, J. W. (2002, October 1). Bridge Inspector’s Reference Manual: Volume 1 and Volume 2. United States Department of Transportation. https://rosap.ntl.bts.gov/view/dot/54492.
Liang, X. (2019). “Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization.” Comput.-Aided Civ. Infrastruct. Eng., 34(5), 415–430.
Liu, P. C.-Y., and El-Gohary, N. (2020). “Semantic Image Retrieval and Clustering for Supporting Domain-Specific Bridge Component and Defect Classification.” Proc., Construction Research Congress 2020: Infrastructure Systems and Sustainability, American Society of Civil Engineers Reston, VA.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). “RoBERTa: A Robustly Optimized BERT Pretraining Approach.”.
Long, J., Shelhamer, E., and Darrell, T. (2015). “Fully Convolutional Networks for Semantic Segmentation”. Proc., IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.
Narazaki, Y., Hoskere, V., Hoang, T. A., Fujino, Y., Sakurai, A., and Spencer, B. F. (2020). “Vision‐Based Automated Bridge Component Recognition with High‐Level Scene Consistency.” Comput.-Aided Civ. Infrastruct. Eng., 35(5), 465–482.
Narazaki, Y., Hoskere, V., Yoshida, K., Spencer, B. F., and Fujino, Y. (2021). “Synthetic Environments for Vision-Based Structural Condition Assessment of Japanese High-Speed Railway Viaducts.” Mech. Syst. Signal Process., 160, 107850.
Ohio Department of Transportation. State of Ohio Bridge Photos. https://brphotos.dot.state.oh.us/.
Prasanna, P., Dana, K. J., Gucunski, N., Basily, B. B., La, H. M., Lim, R. S., and Parvardeh, H. (2016). “Automated Crack Detection on Concrete Bridges.” IEEE Trans. Autom., 13(2), 591–599.
Qi, X., Liu, Z., Shi, J., Zhao, H., and Jia, J. (2016). “Augmented Feedback in Semantic Segmentation Under Image Level Supervision”. Proc., Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Part VIII 14, Springer.
Russell, B. C., Torralba, A., Murphy, K. P., and Freeman, W. T. (2008). “LabelMe: a database and web-based tool for image annotation.” Int. J. Comput. Vis., 77(1), 157–173.
Shan, B., Zheng, S., and Ou, J. (2016). “A stereovision-based crack width detection approach for concrete surface assessment.” KSCE J. Civ. Eng., 20(2), 803–812.
Shorten, C., and Khoshgoftaar, T. M. (2019). “A survey on Image Data Augmentation for Deep Learning.” J. big data, 6(1), 1–48.
Siddique, N., Paheding, S., Elkin, C. P., and Devabhaktuni, V. (2021). “U-net and its variants for medical image segmentation: A review of theory and applications.” IEEE Access, 9, 82031–82057.
Spencer, B. F., Hoskere, V., and Narazaki, Y. (2019). “Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring.” Engineering, 5(2), 199–222.
Sultana, F., Sufian, A., and Dutta, P. (2020). “Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey.” Knowl. Based Syst., 201, 106062.
Talab, A. M. A., Huang, Z., Xi, F., and Haiming, L. (2016). “Detection crack in image using Otsu method and multiple filtering in image processing techniques.” Optik, 127(3), 1030–1033.
Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., and Yang, H. (2022). “Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN.” Sensors, 22(3), 1215.
Xu, Y., Bao, Y., Chen, J., Zuo, W., and Li, H. (2019). “Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images.” Struct. Health Monit., 18(3), 653–674.
Yu, W., and Nishio, M. (2022). “Multilevel Structural Components Detection and Segmentation toward Computer Vision-Based Bridge Inspection.” Sensors, 22(9), 3502.
Zhang, L., Yang, F., Daniel Zhang, Y., and Zhu, Y. J. (2016). “Road crack detection using deep convolutional neural network.” Proc., IEEE Int. Conf. on Image Processing, 2016.
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Published online: Mar 18, 2024
ASCE Technical Topics:
- Architectural engineering
- Bridge components
- Bridge engineering
- Bridge management
- Bridge-vehicle interaction
- Building management
- Computer vision and image processing
- Construction engineering
- Construction management
- Defects and imperfections
- Engineering fundamentals
- Inspection
- Maintenance and operation
- Materials characterization
- Materials engineering
- Methodology (by type)
- Structural engineering
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