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Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms

  • 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-SensorSystem, and -by a fully automated processto 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.

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Metadaten
Author:Matthias Hermann, Frederic Hake, Hamza Alkhatib, Christian Hesse, Karsten Holste, Georg UmlaufORCiDGND, Gaël Kermarrec, Ingo Neumann
URL:https://fig.net/resources/proceedings/fig_proceedings/fig2020/papers/ts08b/TS08B_hake_alkhatib_et_al_10441.pdf
ISBN:978-87-92853-93-6
Parent Title (English):Smart surveyors for land and water management, FIG Working Week 2020, Amsterdam, the Netherlands, 10–14 May 2020
Publisher:International Federation of Surveyors, FIG
Place of publication:Copenhagen
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Release Date:2021/01/15
Tag:Damage Detection; Infrastructure; Laser scanning; Machine-Learning; Multibeam echosounder
Page Number:14
Open Access?:Ja
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)