A steerable pyramid autoencoder based framework for anomaly frame detection of water pipeline CCTV inspection
Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector’s work load and ensure the consistent...
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| Published in: | Measurement : journal of the International Measurement Confederation Vol. 174; p. 109020 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2021
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector’s work load and ensure the consistent quality of the survey. However, a fully automated survey of varied structural discontinuities still remains as a challenge. This study aims to first identify the anomaly frames of the CCTV video, which contain the major anomalies captured from the internal surface of the pipe. Thus, the inspector can focus more on these anomaly frames. In this paper, an anomaly frame detection framework based on steerable pyramid autoencoder (SPAE) is proposed. The SPAE can generate discriminative representations to be used in the prediction. Both the parameter optimization and comparative studies for the proposed SPAE were carried out in this research. The experimental results demonstrate that this novel SPAE algorithm can achieve 0.984 accuracy and 0.984 F1-score, which outperforms other state-of-the-art methods selected for comparison. Thus, the proposed framework can significantly improve the accuracy and efficiency for anomaly frame detection, which will highly facilitate the pipeline condition assessment through the CCTV inspection.
[Display omitted]
•The paper proposes a steerable pyramid autoencoder based framework.•Anomaly frame detection is conducted on closed-circuit television videos.•The framework significantly facilitates the water pipeline condition assessment.•Steerable pyramid is integrated into the autoencoder based network.•The method develops great capability of representation learning and extraction. |
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| AbstractList | Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector’s work load and ensure the consistent quality of the survey. However, a fully automated survey of varied structural discontinuities still remains as a challenge. This study aims to first identify the anomaly frames of the CCTV video, which contain the major anomalies captured from the internal surface of the pipe. Thus, the inspector can focus more on these anomaly frames. In this paper, an anomaly frame detection framework based on steerable pyramid autoencoder (SPAE) is proposed. The SPAE can generate discriminative representations to be used in the prediction. Both the parameter optimization and comparative studies for the proposed SPAE were carried out in this research. The experimental results demonstrate that this novel SPAE algorithm can achieve 0.984 accuracy and 0.984 F1-score, which outperforms other state-of-the-art methods selected for comparison. Thus, the proposed framework can significantly improve the accuracy and efficiency for anomaly frame detection, which will highly facilitate the pipeline condition assessment through the CCTV inspection.
[Display omitted]
•The paper proposes a steerable pyramid autoencoder based framework.•Anomaly frame detection is conducted on closed-circuit television videos.•The framework significantly facilitates the water pipeline condition assessment.•Steerable pyramid is integrated into the autoencoder based network.•The method develops great capability of representation learning and extraction. |
| ArticleNumber | 109020 |
| Author | Jiao, Yutong Wu, Angie Kong, Xiangjie Rayhana, Rakiba Liu, Zheng Bin, Junchi |
| Author_xml | – sequence: 1 givenname: Yutong orcidid: 0000-0003-1181-0589 surname: Jiao fullname: Jiao, Yutong organization: School of Engineering, University of British Columbia, Kelowna BC, Canada – sequence: 2 givenname: Rakiba surname: Rayhana fullname: Rayhana, Rakiba organization: School of Engineering, University of British Columbia, Kelowna BC, Canada – sequence: 3 givenname: Junchi surname: Bin fullname: Bin, Junchi organization: School of Engineering, University of British Columbia, Kelowna BC, Canada – sequence: 4 givenname: Zheng orcidid: 0000-0002-7241-3483 surname: Liu fullname: Liu, Zheng email: zheng.liu@ubc.ca organization: School of Engineering, University of British Columbia, Kelowna BC, Canada – sequence: 5 givenname: Angie surname: Wu fullname: Wu, Angie organization: Pure Technologies, Mississauga ON, Canada – sequence: 6 givenname: Xiangjie surname: Kong fullname: Kong, Xiangjie organization: Pure Technologies, Mississauga ON, Canada |
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| Cites_doi | 10.1109/JSEN.2014.2336240 10.1080/1573062X.2011.617829 10.1016/j.measurement.2012.05.032 10.2166/hydro.2010.144 10.1016/j.autcon.2013.10.012 10.1109/ICIP.1995.537667 10.1109/JSEN.2011.2176484 10.1016/j.neunet.2014.09.003 10.1109/ACCESS.2018.2881003 10.1016/j.compag.2014.12.007 10.3390/s17091967 10.1061/(ASCE)IS.1943-555X.0000161 10.1007/978-3-030-11726-9_28 10.1109/JSEN.2011.2181161 10.1016/j.patcog.2016.03.028 10.1016/j.measurement.2018.10.089 10.1016/S0167-8655(01)00047-2 10.1109/MNET.2011.5687953 |
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| Keywords | Steerable pyramid Deep learning Autoencoder Anomaly frame detection CCTV inspection Classification Water pipeline |
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| References | Ikeda, Ishibashi, Nakano, Watanabe, Kawahara (b22) 2018 E. Simoncelli, W. Freeman, Steerable pyramid: a flexible architecture for multi-scale derivative computation, in: Anon (Ed.), IEEE International Conference on Image Processing, Vol. 3, 1995, pp. 444–447. Erfani, Rajasegarar, Karunasekera, Leckie (b25) 2016; 58 Mounce, Mounce, Boxall (b9) 2011; 13 Rayhana, Jiao, Zaji, Liu (b13) 2020 Lopez-Martin, Carro, Sanchez-Esguevillas, Lloret (b19) 2017; 17 He, Zhang, Ren, Sun (b32) 2016 Chong, Tay (b21) 2017 Liu, Kleiner, Rajani, Wang, Condit (b4) 2012 A. Myronenko, 3D MRI brain tumor segmentation using autoencoder regularization, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Cham, 2019, pp. 311–320. Liu, Ho, Tsukada, Hanasaki, Dai, Li (b29) 2002; 3 Yoon, Ye, Heidemann, Littlefield, Shahabi (b8) 2011; 25 González, Balajewicz (b26) 2018 Liu, Kleiner (b11) 2014; 14 Park, Kim, Lee (b17) 2018; 6 Liu, Kleiner (b1) 2013; 46 Cataldo, Cannazza, De Benedetto, Giaquinto (b10) 2012; 12 (b3) 2020 Liu, Kleiner (b2) 2012; 12 Schmidhuber (b16) 2015; 61 Ma, Li, Wen, Fu, Zhang (b14) 2015; 111 Lu, Xu (b20) 2018 Islam, Sadiq, Rodriguez, Francisque, Najjaran, Hoorfar (b5) 2011; 8 Halfawy, Hengmeechai (b34) 2014; 38 Duran, Althoefer, Seneviratne (b12) 2002 Liu, Tsukada, Hanasaki, Ho, Dai (b28) 2001; 22 (b30) 2008 Ong, Png, Lin, Pua, Rahman (b6) 2017 Ke, Lin, Huang (b18) 2017 Wang, Lu, Yan (b7) 2019; 134 Rayhana, Jiao, Liu, Wu, Kong (b15) 2020 Beggel, Pfeiffer, Bischl (b23) 2019 Zhao, Jia, Lin (b24) 2019 Halfawy, Hengmeechai (b33) 2014; 20 Rayhana (10.1016/j.measurement.2021.109020_b15) 2020 Liu (10.1016/j.measurement.2021.109020_b28) 2001; 22 Islam (10.1016/j.measurement.2021.109020_b5) 2011; 8 Zhao (10.1016/j.measurement.2021.109020_b24) 2019 He (10.1016/j.measurement.2021.109020_b32) 2016 Duran (10.1016/j.measurement.2021.109020_b12) 2002 Rayhana (10.1016/j.measurement.2021.109020_b13) 2020 Ke (10.1016/j.measurement.2021.109020_b18) 2017 Liu (10.1016/j.measurement.2021.109020_b4) 2012 (10.1016/j.measurement.2021.109020_b3) 2020 (10.1016/j.measurement.2021.109020_b30) 2008 Beggel (10.1016/j.measurement.2021.109020_b23) 2019 Halfawy (10.1016/j.measurement.2021.109020_b33) 2014; 20 González (10.1016/j.measurement.2021.109020_b26) 2018 Yoon (10.1016/j.measurement.2021.109020_b8) 2011; 25 Mounce (10.1016/j.measurement.2021.109020_b9) 2011; 13 10.1016/j.measurement.2021.109020_b31 Park (10.1016/j.measurement.2021.109020_b17) 2018; 6 Ong (10.1016/j.measurement.2021.109020_b6) 2017 Ma (10.1016/j.measurement.2021.109020_b14) 2015; 111 Lu (10.1016/j.measurement.2021.109020_b20) 2018 Lopez-Martin (10.1016/j.measurement.2021.109020_b19) 2017; 17 Liu (10.1016/j.measurement.2021.109020_b1) 2013; 46 Schmidhuber (10.1016/j.measurement.2021.109020_b16) 2015; 61 Wang (10.1016/j.measurement.2021.109020_b7) 2019; 134 Liu (10.1016/j.measurement.2021.109020_b29) 2002; 3 Liu (10.1016/j.measurement.2021.109020_b2) 2012; 12 10.1016/j.measurement.2021.109020_b27 Halfawy (10.1016/j.measurement.2021.109020_b34) 2014; 38 Ikeda (10.1016/j.measurement.2021.109020_b22) 2018 Liu (10.1016/j.measurement.2021.109020_b11) 2014; 14 Erfani (10.1016/j.measurement.2021.109020_b25) 2016; 58 Chong (10.1016/j.measurement.2021.109020_b21) 2017 Cataldo (10.1016/j.measurement.2021.109020_b10) 2012; 12 |
| References_xml | – start-page: 2551 year: 2002 end-page: 2556 ident: b12 article-title: Automated sewer pipe inspection through image processing publication-title: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Vol. 3 – volume: 20 year: 2014 ident: b33 article-title: Efficient algorithm for crack detection in sewer images from closed-circuit television inspections publication-title: J. Infrastruct. Syst. – volume: 8 start-page: 351 year: 2011 end-page: 365 ident: b5 article-title: Leakage detection and location in water distribution systems using a fuzzy-based methodology publication-title: Urban Water J. – volume: 14 start-page: 4122 year: 2014 end-page: 4133 ident: b11 article-title: Computational intelligence for urban infrastructure condition assessment: Water transmission and distribution systems publication-title: IEEE Sens. J. – reference: A. Myronenko, 3D MRI brain tumor segmentation using autoencoder regularization, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Cham, 2019, pp. 311–320. – start-page: 189 year: 2017 end-page: 196 ident: b21 article-title: Abnormal event detection in videos using spatiotemporal autoencoder – year: 2019 ident: b23 article-title: Robust anomaly detection in images using adversarial autoencoders – volume: 6 start-page: 70884 year: 2018 end-page: 70901 ident: b17 article-title: Anomaly detection for HTTP using convolutional autoencoders publication-title: IEEE Access – volume: 38 start-page: 1 year: 2014 end-page: 13 ident: b34 article-title: Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine publication-title: Autom. Constr. – start-page: 1 year: 2019 end-page: 21 ident: b24 article-title: Deep Laplacian auto-encoder and its application into imbalanced fault diagnosis of rotating machinery publication-title: Measurement – start-page: 20 year: 2020 end-page: 27 ident: b15 article-title: Water pipe valve detection by using deep neural networks publication-title: Smart Structures and NDE for Industry 4.0, Smart Cities, and Energy Systems, Vol. 11382 – volume: 111 start-page: 92 year: 2015 end-page: 102 ident: b14 article-title: A key frame extraction method for processing greenhouse vegetables production monitoring video publication-title: Comput. Electron. Agric. – volume: 12 start-page: 1987 year: 2012 end-page: 1992 ident: b2 article-title: State-of-the-art review of technologies for pipe structural health monitoring publication-title: IEEE Sens. J. – volume: 46 start-page: 1 year: 2013 end-page: 15 ident: b1 article-title: State of the art review of inspection technologies for condition assessment of water pipes publication-title: Measurement – volume: 3 start-page: 203 year: 2002 end-page: 214 ident: b29 article-title: Using multiple orientational filters of steerable pyramid for image registration publication-title: Int. J. Multi-Sensor Multi-Source Inf. Fusion – volume: 13 start-page: 672 year: 2011 end-page: 686 ident: b9 article-title: Novelty detection for time series data analysis in water distribution systems using support vector machines publication-title: J. Hydroinform. – volume: 12 start-page: 1660 year: 2012 end-page: 1667 ident: b10 article-title: A new method for detecting leaks in underground water pipelines publication-title: IEEE Sens. J. – start-page: 167 year: 2017 end-page: 171 ident: b6 article-title: Acoustic vibration sensor based on macro-bend coated fiber for pipeline leakage detection publication-title: 2017 17th International Conference on Control, Automation and Systems – year: 2020 ident: b3 article-title: Confidently manage your pipes without the need to shutdown or dewater – year: 2018 ident: b20 article-title: Anomaly detection for skin disease images using variational autoencoder – start-page: 770 year: 2016 end-page: 778 ident: b32 article-title: Deep residual learning for image recognition publication-title: 2016 IEEE Conference on Computer Vision and Pattern Recognition – start-page: 1 year: 2020 end-page: 18 ident: b13 article-title: Automated vision systems for condition assessment of sewer and water pipelines publication-title: IEEE Trans. Autom. Sci. Eng. – reference: E. Simoncelli, W. Freeman, Steerable pyramid: a flexible architecture for multi-scale derivative computation, in: Anon (Ed.), IEEE International Conference on Image Processing, Vol. 3, 1995, pp. 444–447. – volume: 134 start-page: 326 year: 2019 end-page: 335 ident: b7 article-title: Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis publication-title: Measurement – year: 2018 ident: b26 article-title: Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems – volume: 17 start-page: 1967 year: 2017 ident: b19 article-title: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT publication-title: Sensors – start-page: 1163 year: 2017 end-page: 1168 ident: b18 article-title: Anomaly detection of logo images in the mobile phone using convolutional autoencoder publication-title: 2017 4th International Conference on Systems and Informatics – year: 2018 ident: b22 article-title: Anomaly detection and interpretation using multimodal autoencoder and sparse optimization – volume: 25 start-page: 50 year: 2011 end-page: 56 ident: b8 article-title: SWATS: Wireless sensor networks for steamflood and waterflood pipeline monitoring publication-title: IEEE Netw. – year: 2012 ident: b4 article-title: Condition Assessment Technologies for Water Transmission and Distribution Systems – year: 2008 ident: b30 article-title: The steerable pyramid – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: b16 article-title: Deep learning in neural networks: An overview publication-title: Neural Netw. – volume: 58 start-page: 121 year: 2016 end-page: 134 ident: b25 article-title: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning publication-title: Pattern Recognit. – volume: 22 start-page: 928 year: 2001 end-page: 937 ident: b28 article-title: Image fusion by using steerable pyramid publication-title: Pattern Recognit. Lett. – year: 2012 ident: 10.1016/j.measurement.2021.109020_b4 – volume: 14 start-page: 4122 issue: 12 year: 2014 ident: 10.1016/j.measurement.2021.109020_b11 article-title: Computational intelligence for urban infrastructure condition assessment: Water transmission and distribution systems publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2014.2336240 – start-page: 20 year: 2020 ident: 10.1016/j.measurement.2021.109020_b15 article-title: Water pipe valve detection by using deep neural networks – start-page: 770 year: 2016 ident: 10.1016/j.measurement.2021.109020_b32 article-title: Deep residual learning for image recognition – year: 2020 ident: 10.1016/j.measurement.2021.109020_b3 – volume: 8 start-page: 351 issue: 6 year: 2011 ident: 10.1016/j.measurement.2021.109020_b5 article-title: Leakage detection and location in water distribution systems using a fuzzy-based methodology publication-title: Urban Water J. doi: 10.1080/1573062X.2011.617829 – volume: 46 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.measurement.2021.109020_b1 article-title: State of the art review of inspection technologies for condition assessment of water pipes publication-title: Measurement doi: 10.1016/j.measurement.2012.05.032 – volume: 13 start-page: 672 issue: 4 year: 2011 ident: 10.1016/j.measurement.2021.109020_b9 article-title: Novelty detection for time series data analysis in water distribution systems using support vector machines publication-title: J. Hydroinform. doi: 10.2166/hydro.2010.144 – volume: 38 start-page: 1 year: 2014 ident: 10.1016/j.measurement.2021.109020_b34 article-title: Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine publication-title: Autom. Constr. doi: 10.1016/j.autcon.2013.10.012 – ident: 10.1016/j.measurement.2021.109020_b27 doi: 10.1109/ICIP.1995.537667 – volume: 12 start-page: 1660 issue: 6 year: 2012 ident: 10.1016/j.measurement.2021.109020_b10 article-title: A new method for detecting leaks in underground water pipelines publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2011.2176484 – volume: 61 start-page: 85 year: 2015 ident: 10.1016/j.measurement.2021.109020_b16 article-title: Deep learning in neural networks: An overview publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.09.003 – volume: 6 start-page: 70884 year: 2018 ident: 10.1016/j.measurement.2021.109020_b17 article-title: Anomaly detection for HTTP using convolutional autoencoders publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2881003 – year: 2018 ident: 10.1016/j.measurement.2021.109020_b20 – start-page: 189 year: 2017 ident: 10.1016/j.measurement.2021.109020_b21 – volume: 111 start-page: 92 year: 2015 ident: 10.1016/j.measurement.2021.109020_b14 article-title: A key frame extraction method for processing greenhouse vegetables production monitoring video publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2014.12.007 – volume: 17 start-page: 1967 issue: 9 year: 2017 ident: 10.1016/j.measurement.2021.109020_b19 article-title: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT publication-title: Sensors doi: 10.3390/s17091967 – volume: 20 year: 2014 ident: 10.1016/j.measurement.2021.109020_b33 article-title: Efficient algorithm for crack detection in sewer images from closed-circuit television inspections publication-title: J. Infrastruct. Syst. doi: 10.1061/(ASCE)IS.1943-555X.0000161 – start-page: 1 year: 2019 ident: 10.1016/j.measurement.2021.109020_b24 article-title: Deep Laplacian auto-encoder and its application into imbalanced fault diagnosis of rotating machinery publication-title: Measurement – start-page: 167 year: 2017 ident: 10.1016/j.measurement.2021.109020_b6 article-title: Acoustic vibration sensor based on macro-bend coated fiber for pipeline leakage detection – ident: 10.1016/j.measurement.2021.109020_b31 doi: 10.1007/978-3-030-11726-9_28 – volume: 12 start-page: 1987 issue: 6 year: 2012 ident: 10.1016/j.measurement.2021.109020_b2 article-title: State-of-the-art review of technologies for pipe structural health monitoring publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2011.2181161 – year: 2008 ident: 10.1016/j.measurement.2021.109020_b30 – volume: 58 start-page: 121 year: 2016 ident: 10.1016/j.measurement.2021.109020_b25 article-title: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.03.028 – start-page: 1163 year: 2017 ident: 10.1016/j.measurement.2021.109020_b18 article-title: Anomaly detection of logo images in the mobile phone using convolutional autoencoder – year: 2018 ident: 10.1016/j.measurement.2021.109020_b26 – start-page: 1 year: 2020 ident: 10.1016/j.measurement.2021.109020_b13 article-title: Automated vision systems for condition assessment of sewer and water pipelines publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 3 start-page: 203 issue: 3 year: 2002 ident: 10.1016/j.measurement.2021.109020_b29 article-title: Using multiple orientational filters of steerable pyramid for image registration publication-title: Int. J. Multi-Sensor Multi-Source Inf. Fusion – volume: 134 start-page: 326 year: 2019 ident: 10.1016/j.measurement.2021.109020_b7 article-title: Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis publication-title: Measurement doi: 10.1016/j.measurement.2018.10.089 – volume: 22 start-page: 928 issue: 9 year: 2001 ident: 10.1016/j.measurement.2021.109020_b28 article-title: Image fusion by using steerable pyramid publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(01)00047-2 – volume: 25 start-page: 50 issue: 1 year: 2011 ident: 10.1016/j.measurement.2021.109020_b8 article-title: SWATS: Wireless sensor networks for steamflood and waterflood pipeline monitoring publication-title: IEEE Netw. doi: 10.1109/MNET.2011.5687953 – start-page: 2551 year: 2002 ident: 10.1016/j.measurement.2021.109020_b12 article-title: Automated sewer pipe inspection through image processing – year: 2019 ident: 10.1016/j.measurement.2021.109020_b23 – year: 2018 ident: 10.1016/j.measurement.2021.109020_b22 |
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| Snippet | Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video... |
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| SubjectTerms | Anomaly frame detection Autoencoder CCTV inspection Classification Deep learning Steerable pyramid Water pipeline |
| Title | A steerable pyramid autoencoder based framework for anomaly frame detection of water pipeline CCTV inspection |
| URI | https://dx.doi.org/10.1016/j.measurement.2021.109020 |
| Volume | 174 |
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