A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes
Identifying and locating sewer defects is crucial for minimizing the risk of sewer-related accidents. Currently, spatial localization of sewer defects in closed circuit television (CCTV) inspection data is primarily performed through manual visual inspections by professional technicians, which is ti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11087227/ |
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| author | Jun Tang Jisheng Xia Zhiqiang Xie Zhaoyong Li Yuting Zhang |
| author_facet | Jun Tang Jisheng Xia Zhiqiang Xie Zhaoyong Li Yuting Zhang |
| author_sort | Jun Tang |
| collection | DOAJ |
| description | Identifying and locating sewer defects is crucial for minimizing the risk of sewer-related accidents. Currently, spatial localization of sewer defects in closed circuit television (CCTV) inspection data is primarily performed through manual visual inspections by professional technicians, which is time-consuming and costly. Consequently, there is an urgent need to develop automated solutions. This study proposes an innovative method for automatic defect location, which involves defect tracking based on rules, calculating the distance of the defect relative to the pipe endpoint, and converting this distance into physical world coordinates. Following experimental validation, the multiple object tracking accuracy (MOTA) metric of defect tracking within the proposed method ranged from -1.46 to 0.89 across ten test videos. The proposed method can be easily integrated into practical engineering applications, thereby alleviating the workload of professional technicians in obtaining the geographic locations of sewer defects in CCTV inspection data and reducing both time and labor costs associated with this process. |
| format | Article |
| id | doaj-art-c9118e7c61be48f3a46cc19c20e2bf45 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c9118e7c61be48f3a46cc19c20e2bf452025-08-20T03:40:17ZengIEEEIEEE Access2169-35362025-01-011313593113594910.1109/ACCESS.2025.359103111087227A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer PipesJun Tang0Jisheng Xia1https://orcid.org/0000-0001-7498-5509Zhiqiang Xie2https://orcid.org/0000-0001-7084-7352Zhaoyong Li3Yuting Zhang4https://orcid.org/0009-0002-1577-8192Institute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaUrban Underground Space Planning Management Office of Kunming City, Kunming, ChinaSchool of Earth Sciences, Yunnan University, Kunming, ChinaIdentifying and locating sewer defects is crucial for minimizing the risk of sewer-related accidents. Currently, spatial localization of sewer defects in closed circuit television (CCTV) inspection data is primarily performed through manual visual inspections by professional technicians, which is time-consuming and costly. Consequently, there is an urgent need to develop automated solutions. This study proposes an innovative method for automatic defect location, which involves defect tracking based on rules, calculating the distance of the defect relative to the pipe endpoint, and converting this distance into physical world coordinates. Following experimental validation, the multiple object tracking accuracy (MOTA) metric of defect tracking within the proposed method ranged from -1.46 to 0.89 across ten test videos. The proposed method can be easily integrated into practical engineering applications, thereby alleviating the workload of professional technicians in obtaining the geographic locations of sewer defects in CCTV inspection data and reducing both time and labor costs associated with this process.https://ieeexplore.ieee.org/document/11087227/Sewer defectsdefect detectionanomaly detectionobject trackingcomputer vision |
| spellingShingle | Jun Tang Jisheng Xia Zhiqiang Xie Zhaoyong Li Yuting Zhang A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes IEEE Access Sewer defects defect detection anomaly detection object tracking computer vision |
| title | A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes |
| title_full | A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes |
| title_fullStr | A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes |
| title_full_unstemmed | A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes |
| title_short | A Method for Automatically Locating Defects in CCTV Inspection Data of Sewer Pipes |
| title_sort | method for automatically locating defects in cctv inspection data of sewer pipes |
| topic | Sewer defects defect detection anomaly detection object tracking computer vision |
| url | https://ieeexplore.ieee.org/document/11087227/ |
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