Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images
Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent managem...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3199 |
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| author | Lili Zhang Zihan Jin Yibo Wang Ziyi Wang Zeyu Duan Taoran Qi Rui Shi |
| author_facet | Lili Zhang Zihan Jin Yibo Wang Ziyi Wang Zeyu Duan Taoran Qi Rui Shi |
| author_sort | Lili Zhang |
| collection | DOAJ |
| description | Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent management for regular inspections. Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. We also designed physics-informed neural networks (PINNs) with multi-subnets to compute the temperature value automatically. Secondly, we obtained the time-domain features in one-dimensional space and used the diffusion techniques to obtain the synthetic infrared images with dam hollowing by converting the one-dimensional temperatures into two-dimensional ones. Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. Our method achieved 94.7% of recognition accuracy based on few shots real images, which is 17.9% and 5.8% higher than maximum entropy and classical OTSU methods, respectively. Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection. |
| format | Article |
| id | doaj-art-a8e828cc014448c9bb2c8d1f314db829 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-a8e828cc014448c9bb2c8d1f314db8292025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510319910.3390/s25103199Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared ImagesLili Zhang0Zihan Jin1Yibo Wang2Ziyi Wang3Zeyu Duan4Taoran Qi5Rui Shi6College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, ChinaCollege of Information Science and Engineering, Hohai University, Changzhou 213200, ChinaCollege of Information Science and Engineering, Hohai University, Changzhou 213200, ChinaCollege of Information Science and Engineering, Hohai University, Changzhou 213200, ChinaCollege of Information Science and Engineering, Hohai University, Changzhou 213200, ChinaCollege of Information Science and Engineering, Hohai University, Changzhou 213200, ChinaThe National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaConcrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent management for regular inspections. Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. We also designed physics-informed neural networks (PINNs) with multi-subnets to compute the temperature value automatically. Secondly, we obtained the time-domain features in one-dimensional space and used the diffusion techniques to obtain the synthetic infrared images with dam hollowing by converting the one-dimensional temperatures into two-dimensional ones. Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. Our method achieved 94.7% of recognition accuracy based on few shots real images, which is 17.9% and 5.8% higher than maximum entropy and classical OTSU methods, respectively. Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection.https://www.mdpi.com/1424-8220/25/10/3199spatio-temporal infrared featuresnon-destructive inspectionhollowing detectioninfrared imageslow-data learningadaptive joint learning |
| spellingShingle | Lili Zhang Zihan Jin Yibo Wang Ziyi Wang Zeyu Duan Taoran Qi Rui Shi Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images Sensors spatio-temporal infrared features non-destructive inspection hollowing detection infrared images low-data learning adaptive joint learning |
| title | Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images |
| title_full | Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images |
| title_fullStr | Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images |
| title_full_unstemmed | Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images |
| title_short | Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images |
| title_sort | novel spatio temporal joint learning based intelligent hollowing detection in dams for low data infrared images |
| topic | spatio-temporal infrared features non-destructive inspection hollowing detection infrared images low-data learning adaptive joint learning |
| url | https://www.mdpi.com/1424-8220/25/10/3199 |
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