An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels
Bolts, as the basic units of tunnel linings, are crucial to safe tunnel service. Caused by the moist and complex environment in the tunnel, corrosion becomes a significant defect of bolts. Computer vision technology is adopted because manual patrol inspection is inefficient and often misses the corr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9715 |
| _version_ | 1797455383355195392 |
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| author | Lei Tan Tao Tang Dajun Yuan |
| author_facet | Lei Tan Tao Tang Dajun Yuan |
| author_sort | Lei Tan |
| collection | DOAJ |
| description | Bolts, as the basic units of tunnel linings, are crucial to safe tunnel service. Caused by the moist and complex environment in the tunnel, corrosion becomes a significant defect of bolts. Computer vision technology is adopted because manual patrol inspection is inefficient and often misses the corroded bolts. However, most current studies are conducted in a laboratory with good lighting conditions, while their effects in actual practice have yet to be considered, and the accuracy also needs to be improved. In this paper, we put forward an Ensemble Learning approach combining our Improved MultiScale Retinex with Color Restoration (IMSRCR) and You Only Look Once (YOLO) based on truly acquired tunnel image data to detect corroded bolts in the lining. The IMSRCR sharpens and strengthens the features of the lining pictures, weakening the bad effect of a dim environment compared with the existing MSRCR. Furthermore, we combine models with different parameters that show different performance using the ensemble learning method, greatly improving the accuracy. Sufficient comparisons and ablation experiments based on a dataset collected from the tunnel in service are conducted to prove the superiority of our proposed algorithm. |
| first_indexed | 2024-03-09T15:52:39Z |
| format | Article |
| id | doaj.art-7eba72684f3a48bdb4d0ec4989235399 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:39Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7eba72684f3a48bdb4d0ec49892353992023-11-24T17:54:08ZengMDPI AGSensors1424-82202022-12-012224971510.3390/s22249715An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in TunnelsLei Tan0Tao Tang1Dajun Yuan2State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBolts, as the basic units of tunnel linings, are crucial to safe tunnel service. Caused by the moist and complex environment in the tunnel, corrosion becomes a significant defect of bolts. Computer vision technology is adopted because manual patrol inspection is inefficient and often misses the corroded bolts. However, most current studies are conducted in a laboratory with good lighting conditions, while their effects in actual practice have yet to be considered, and the accuracy also needs to be improved. In this paper, we put forward an Ensemble Learning approach combining our Improved MultiScale Retinex with Color Restoration (IMSRCR) and You Only Look Once (YOLO) based on truly acquired tunnel image data to detect corroded bolts in the lining. The IMSRCR sharpens and strengthens the features of the lining pictures, weakening the bad effect of a dim environment compared with the existing MSRCR. Furthermore, we combine models with different parameters that show different performance using the ensemble learning method, greatly improving the accuracy. Sufficient comparisons and ablation experiments based on a dataset collected from the tunnel in service are conducted to prove the superiority of our proposed algorithm.https://www.mdpi.com/1424-8220/22/24/9715corroded bolt detectioncomputer visioncolor enhancementensemble learning |
| spellingShingle | Lei Tan Tao Tang Dajun Yuan An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels Sensors corroded bolt detection computer vision color enhancement ensemble learning |
| title | An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels |
| title_full | An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels |
| title_fullStr | An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels |
| title_full_unstemmed | An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels |
| title_short | An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels |
| title_sort | ensemble learning aided computer vision method with advanced color enhancement for corroded bolt detection in tunnels |
| topic | corroded bolt detection computer vision color enhancement ensemble learning |
| url | https://www.mdpi.com/1424-8220/22/24/9715 |
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