Jahan, Kanwal und Lähns, Alexander und Baasch, Benjamin und Heusel, Judith und Roth, Michael (2023) Rail Surface Defect Detection and Severity Analysis using CNNs on Camera and Axle Box Acceleration Data. In: IAI2023 - 7th International Congress and Workshop on Industrial AI and eMaintenance. IAI2023 - 7th International Congress and Workshop on Industrial AI and eMaintenance, 2023-06-13 - 2023-06-15, Luleå, Sweden.
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Kurzfassung
Rail surface defect detection is a relevant problem in the field of data-driven railway maintenance. Artificial intelligence and neural networks (NN) for axle box acceleration (ABA) or camera data show great potential for defect detection and classification. However, a sufficient amount of labeled training data is required, all the more if the defect severity is to be estimated. A unique dataset of time-synchronized ABA and camera data is employed that contains labeled defect instances. For the image analysis, RetinaNet as a single-stage object detector (with the backbone of ResNet-50 and a feature pyramid network) is used to achieve high classification performance for the two most common rail surface defects (squat and corrugation). Additionally, a machine learning-based method on ABA data to estimate defect severity levels (low, medium, heavy) is proposed. False positives are detected in the original labels by both classifiers during evaluation. The inspection of the false positives in image data reveals that defects have been overlooked in the initial labeling. The insights of this work help to reduce the dependency on labeled data by using only a few labeled samples and by exploiting complementary data sources instead of increasing the number of labeled instances.
elib-URL des Eintrags: | https://elib.dlr.de/194005/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||
Titel: | Rail Surface Defect Detection and Severity Analysis using CNNs on Camera and Axle Box Acceleration Data | ||||||||||||||||||||||||
Autoren: |
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Datum: | 2023 | ||||||||||||||||||||||||
Erschienen in: | IAI2023 - 7th International Congress and Workshop on Industrial AI and eMaintenance | ||||||||||||||||||||||||
Referierte Publikation: | Nein | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
Herausgeber: |
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Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Deep learning, Convolutional neural network, Time-synchronized dataset, Supervised learning, Severity analysis, Rail surface defects, Squats, Corrugation | ||||||||||||||||||||||||
Veranstaltungstitel: | IAI2023 - 7th International Congress and Workshop on Industrial AI and eMaintenance | ||||||||||||||||||||||||
Veranstaltungsort: | Luleå, Sweden | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 13 Juni 2023 | ||||||||||||||||||||||||
Veranstaltungsende: | 15 Juni 2023 | ||||||||||||||||||||||||
Veranstalter : | The Division of Operation and Maintenance Engineering, Luleå University of Technology | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Verkehr | ||||||||||||||||||||||||
HGF - Programmthema: | Schienenverkehr | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | V SC Schienenverkehr | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | V - TraCo - Train Control and Management | ||||||||||||||||||||||||
Standort: | Berlin-Adlershof , Braunschweig | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Verkehrssystemtechnik > Informationsgewinnung und Modellierung, BA Institut für Verkehrssystemtechnik > Informationsgewinnung und Modellierung, BS | ||||||||||||||||||||||||
Hinterlegt von: | Baasch, Dr. Benjamin | ||||||||||||||||||||||||
Hinterlegt am: | 11 Dez 2023 14:50 | ||||||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:54 |
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