Using Machine-Learning for the Damage Detection of Harbour Structures
The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2518 |
| _version_ | 1827664169125543936 |
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| author | Frederic Hake Leonard Göttert Ingo Neumann Hamza Alkhatib |
| author_facet | Frederic Hake Leonard Göttert Ingo Neumann Hamza Alkhatib |
| author_sort | Frederic Hake |
| collection | DOAJ |
| description | The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings. |
| first_indexed | 2024-03-10T00:55:26Z |
| format | Article |
| id | doaj.art-0e2356288acc44a98d79c220e27be4b9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:55:26Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0e2356288acc44a98d79c220e27be4b92023-11-23T14:43:00ZengMDPI AGRemote Sensing2072-42922022-05-011411251810.3390/rs14112518Using Machine-Learning for the Damage Detection of Harbour StructuresFrederic Hake0Leonard Göttert1Ingo Neumann2Hamza Alkhatib3Geodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, GermanyGeodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, GermanyGeodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, GermanyGeodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, GermanyThe ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings.https://www.mdpi.com/2072-4292/14/11/2518damage detectionmachine-learninginfrastructurelaserscanningmultibeam echo-sounder |
| spellingShingle | Frederic Hake Leonard Göttert Ingo Neumann Hamza Alkhatib Using Machine-Learning for the Damage Detection of Harbour Structures Remote Sensing damage detection machine-learning infrastructure laserscanning multibeam echo-sounder |
| title | Using Machine-Learning for the Damage Detection of Harbour Structures |
| title_full | Using Machine-Learning for the Damage Detection of Harbour Structures |
| title_fullStr | Using Machine-Learning for the Damage Detection of Harbour Structures |
| title_full_unstemmed | Using Machine-Learning for the Damage Detection of Harbour Structures |
| title_short | Using Machine-Learning for the Damage Detection of Harbour Structures |
| title_sort | using machine learning for the damage detection of harbour structures |
| topic | damage detection machine-learning infrastructure laserscanning multibeam echo-sounder |
| url | https://www.mdpi.com/2072-4292/14/11/2518 |
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