Automated Detection and Quantification of Drainage Pipe Cracks in Closed-Circuit Television (CCTV) Images
Publication: World Environmental and Water Resources Congress 2024
ABSTRACT
Closed-circuit television (CCTV) is a common method for drainage pipe inspection, and currently, the interpretation of CCTV images is mostly conducted manually. In this study, an integrated algorithm, namely DeepLab V3 Plus-Crack Length Quantification (DL-CLQ), is proposed to detect and quantify pipe cracks, which combines a semantic segmentation model and the newly developed crack length quantification algorithm. The proposed algorithm is verified by artificially created pipe cracks. In the artificial scenarios, DL-CLQ shows a mIoU higher than based-line models in segmentation, and lower in MSE of crack quantification, which indicates the accuracy of crack quantification depends greatly on the segmentation accuracy. This study provides an innovative method for automatic drainage pipe defect detection and quantification and can also contribute to the further development of smart management for urban drainage networks.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
MHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China). (2017). Statistical Yearbook of Urban Construction.
Cheng, J. C. P., and Wang, M. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques[J]. Automation in Construction. 2018, 95: 155–171.
Haurum, J. B., and Moeslund, T. B. A survey on image-based automation of CCTV and SSET sewer inspections[J]. Automation in Construction. 2020, 111.
Halfawy, M. R., and Hengmeechai, J. Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine[J]. Automation in Construction. 2014, 38: 1–13.
Mashford, J., Rahilly, M., and Davis, P. A morphological approach to pipe image interpretation based on segmentation by support vector machine[J]. Automation in Construction. 2010, 19(7): 875–883.
Cao, T., Zhou, L., and Qu, L. Aircraft pipe gap inspection on raw point cloud from a single view[J]. IEEE Transactions on Instrumentation and Measurement. 2022, 71.
Yin, C., Cheng, J. C. P., and Wang, B. Automated classification of piping components from 3D LiDAR point clouds using SE-PseudoGrid[J]. Automation in Construction. 2022, 139.
Tian, Y., Ding, C., and Lin, Y. F. Automatic feature type selection in digital photogrammetry of piping[J]. Computer-Aided Civil and Infrastructure Engineering. 2022, 37(10): 1335–1348.
Su, T., Yang, M., and Wu, T. Morphological segmentation based on edge detection for sewer pipe defects on CCTV images[J]. Expert Systems with Applications. 2011, 38(10): 13094–13114.
Iyer, S., and Sinha, S. K. Segmentation of pipe images for crack detection in buried sewers[J]. Computer-Aided Civil and Infrastructure Engineering. 2006, 21(6): 395–410.
Yang, M., and Su, T. Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis[J]. Expert Systems with Applications. 2009, 36(2): 3562–3573.
Altabey, W. A., Noori, M., and Wang, T. Deep learning-based crack identification for steel pipelines by extracting features from 3D shadow modeling[J]. Applied Sciences. 2021, 11(13): 6063.
Wang, M., and Cheng, J. C. P. A unified convolutional neural network integrated with conditional random field for pipe defect segmentation[J]. Computer-Aided Civil and Infrastructure Engineering. 2019, 35(2): 162–177.
Altabey, W. A., Noori, M., and Wang, T. Deep learning-based crack identification for steel pipelines by extracting features from 3D shadow modeling[J]. Applied Sciences. 2021, 11(13): 6063.
Ma, D., Fang, H. Y., and Wang, N. N. Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects[J]. Automation in Construction. 2022, 144.
Pan, G., Zheng, Y., and Guo, S., et al. Automatic sewer pipe defect semantic segmentation based on improved U-Net[J]. Automation in Construction. 2020, 119: 103383.
Wang, M. Z., Luo, H., and Cheng, J. Towards an automated condition assessment algorithm of underground sewer pipes based on closed-circuit television (CCTV) images[J]. Tunnelling and Underground Space Technology. 2021, 110.
Torralba, A., Russell, B. C., and Yuen, J. LabelMe: Online Image Annotation and Applications[J]. Proceedings of the IEEE. 2010, 98(8): 1467–1484.
Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2024
Pages: 268 - 282
History
Published online: May 16, 2024
ASCE Technical Topics:
- Algorithms
- Automation and robotics
- Continuum mechanics
- Cracking
- Detection methods
- Drainage
- Engineering fundamentals
- Engineering mechanics
- Fracture mechanics
- Infrastructure
- Irrigation engineering
- Mathematics
- Methodology (by type)
- Model accuracy
- Models (by type)
- Pipeline systems
- Pipes
- Solid mechanics
- Systems engineering
- Water and water resources
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.