Details
| Original language | English |
|---|---|
| Publication status | E-pub ahead of print - 2020 |
| Event | FIG Working Week 2020: Smart surveyors for land and water management - Amsterdam, Netherlands Duration: 10 May 2020 → 14 May 2020 |
Conference
| Conference | FIG Working Week 2020 |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 10 May 2020 → 14 May 2020 |
Abstract
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2020. Paper presented at FIG Working Week 2020, Amsterdam, Netherlands.
Research output: Contribution to conference › Paper › Research
}
TY - CONF
T1 - Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms
AU - Hake, Frederic
AU - Hermann, Matthias
AU - Alkhatib, Hamza
AU - Hesse, Christian
AU - Holste, Karsten
AU - Umlauf, Georg
AU - Kermarrec, Gael
AU - Neumann, Ingo
PY - 2020
Y1 - 2020
N2 - The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense by hand the entire underwater infrastructure. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose to scan the above and underwater port structure with a Multi-Sensor-System, and -by a fully automated process-to classify the obtained point cloud into damaged and undamaged zones. We make use of simulated training data to test our approach since not enough training data with corresponding class labels are available yet. To that aim, we build a rasterised heightfield of a point cloud of a sheet pile wall by cutting it into verticall slices. The distance from each slice to the corresponding line generates the heightfield. This latter is propagated through a convolutional neural network which detects anomalies. We use the VGG19 Deep Neural Network model pretrained on natural images. This neural network has 19 layers and it is often used for image recognition tasks. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection which is able to analyse the whole structure instead of the sample wise manual method with divers. The mean true positive rate is 0.98 which means that we detected 98 % of the damages in the simulated environment.
AB - The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense by hand the entire underwater infrastructure. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose to scan the above and underwater port structure with a Multi-Sensor-System, and -by a fully automated process-to classify the obtained point cloud into damaged and undamaged zones. We make use of simulated training data to test our approach since not enough training data with corresponding class labels are available yet. To that aim, we build a rasterised heightfield of a point cloud of a sheet pile wall by cutting it into verticall slices. The distance from each slice to the corresponding line generates the heightfield. This latter is propagated through a convolutional neural network which detects anomalies. We use the VGG19 Deep Neural Network model pretrained on natural images. This neural network has 19 layers and it is often used for image recognition tasks. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection which is able to analyse the whole structure instead of the sample wise manual method with divers. The mean true positive rate is 0.98 which means that we detected 98 % of the damages in the simulated environment.
M3 - Paper
T2 - FIG Working Week 2020
Y2 - 10 May 2020 through 14 May 2020
ER -