Details
| Original language | English |
|---|---|
| Article number | 2518 |
| Journal | Remote sensing |
| Volume | 14 |
| Issue number | 11 |
| Publication status | Published - 24 May 2022 |
Abstract
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.
Keywords
- damage detection, infrastructure, laserscanning, machine-learning, multibeam echo-sounder
ASJC Scopus subject areas
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In: Remote sensing, Vol. 14, No. 11, 2518, 24.05.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Using Machine-Learning for the Damage Detection of Harbour Structures
AU - Hake, Frederic
AU - Göttert, Leonard
AU - Neumann, Ingo
AU - Alkhatib, Hamza
N1 - Funding Information: Funding: This research was funded by Federal Ministry of Transport and Digital Infrastructure grant number 19H18011C. The publication of this article was funded by the Open Access Fund of the Leibniz University Hannover.
PY - 2022/5/24
Y1 - 2022/5/24
N2 - 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.
AB - 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.
KW - damage detection
KW - infrastructure
KW - laserscanning
KW - machine-learning
KW - multibeam echo-sounder
UR - http://www.scopus.com/inward/record.url?scp=85131604110&partnerID=8YFLogxK
U2 - 10.3390/rs14112518
DO - 10.3390/rs14112518
M3 - Article
VL - 14
JO - Remote sensing
JF - Remote sensing
SN - 2072-4292
IS - 11
M1 - 2518
ER -