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Convolutional Neural Networks for Detecting Bridge Crossing Events with Ground-Based Interferometric Radar Data

Arnold, M.; Hoyer, M.; Keller, S.

Abstract:
This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000139619
Veröffentlicht am 04.11.2021
Originalveröffentlichung
DOI: 10.5194/isprs-annals-V-1-2021-31-2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2194-9050
KITopen-ID: 1000139619
Erschienen in ISPRS annals
Verlag Copernicus Publications
Band V-1-2021
Seiten 31–38
Vorab online veröffentlicht am 17.06.2021
Schlagwörter Ground-based Interferometric Radar, Event Detection, CNN, Infrastructure Monitoring, Machine Learning, Time series classification, Field Campaign, UAV
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