Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences
Most existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these me...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9007362/ |
| _version_ | 1819175821792772096 |
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| author | Xu Fang Wenhao Guo Qingquan Li Jiasong Zhu Zhipeng Chen Jianwei Yu Baoding Zhou Haokun Yang |
| author_facet | Xu Fang Wenhao Guo Qingquan Li Jiasong Zhu Zhipeng Chen Jianwei Yu Baoding Zhou Haokun Yang |
| author_sort | Xu Fang |
| collection | DOAJ |
| description | Most existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these methods have limitations due to the presence of unpredictable sewer pipeline fault patterns. Deep learning methods have also been applied to sewer pipeline fault detection. However, these methods require a large amount of annotated data to obtain reliable results. In this paper, we propose a fault detection method that applies unsupervised machine learning based anomaly detection algorithms with feature extraction to videos recorded by new sewer pipeline visual inspection equipment. The recorded videos are regarded as sequence signals, which are converted into feature vectors, followed by application of an anomaly detection algorithm. Unlike existing methods, the proposed method is computationally efficient as it does not require an annotated fault sample database for training fault detection models. We evaluate various anomaly detection algorithms and feature combinations on real sewer pipeline data collected in Shenzhen, with an overall accuracy result of above 90%. The proposed method provides a new and fast technique for surveying urban sewer pipelines, and to facilitate further research in this area, we have made the code and data used in this paper publicly available. |
| first_indexed | 2024-12-22T21:00:58Z |
| format | Article |
| id | doaj.art-782991e524a0482f9b8f536150379606 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:00:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-782991e524a0482f9b8f5361503796062022-12-21T18:12:50ZengIEEEIEEE Access2169-35362020-01-018395743958610.1109/ACCESS.2020.29758879007362Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video SequencesXu Fang0https://orcid.org/0000-0002-6762-311XWenhao Guo1https://orcid.org/0000-0002-3261-1864Qingquan Li2https://orcid.org/0000-0002-2438-6046Jiasong Zhu3https://orcid.org/0000-0001-6177-3363Zhipeng Chen4https://orcid.org/0000-0001-8537-2409Jianwei Yu5https://orcid.org/0000-0002-9364-1784Baoding Zhou6https://orcid.org/0000-0003-1607-2626Haokun Yang7https://orcid.org/0000-0002-9293-1405MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen, ChinaMost existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these methods have limitations due to the presence of unpredictable sewer pipeline fault patterns. Deep learning methods have also been applied to sewer pipeline fault detection. However, these methods require a large amount of annotated data to obtain reliable results. In this paper, we propose a fault detection method that applies unsupervised machine learning based anomaly detection algorithms with feature extraction to videos recorded by new sewer pipeline visual inspection equipment. The recorded videos are regarded as sequence signals, which are converted into feature vectors, followed by application of an anomaly detection algorithm. Unlike existing methods, the proposed method is computationally efficient as it does not require an annotated fault sample database for training fault detection models. We evaluate various anomaly detection algorithms and feature combinations on real sewer pipeline data collected in Shenzhen, with an overall accuracy result of above 90%. The proposed method provides a new and fast technique for surveying urban sewer pipelines, and to facilitate further research in this area, we have made the code and data used in this paper publicly available.https://ieeexplore.ieee.org/document/9007362/Anomaly detectionsewer pipelinefeature extractionfault detection |
| spellingShingle | Xu Fang Wenhao Guo Qingquan Li Jiasong Zhu Zhipeng Chen Jianwei Yu Baoding Zhou Haokun Yang Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences IEEE Access Anomaly detection sewer pipeline feature extraction fault detection |
| title | Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences |
| title_full | Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences |
| title_fullStr | Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences |
| title_full_unstemmed | Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences |
| title_short | Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences |
| title_sort | sewer pipeline fault identification using anomaly detection algorithms on video sequences |
| topic | Anomaly detection sewer pipeline feature extraction fault detection |
| url | https://ieeexplore.ieee.org/document/9007362/ |
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