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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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 174; S. 109020 |
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| Sprache: | Englisch |
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01.04.2021
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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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| Keywords | Steerable pyramid Deep learning Autoencoder Anomaly frame detection CCTV inspection Classification Water pipeline |
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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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