How to get pavement distress detection ready for deep learning? A systematic approach

  • Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.

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Metadaten
Author:Markus Eisenbach, Ronny Stricker, Daniel Seichter, Karl Amende, Klaus Debes, Maximilian Sesselmann, Dirk Ebersbach, Ulrike Stöckert, Horst Michael Gross
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2018/09/25
Contributing corporation:Technische Universität Ilmenau. Neuroinformatics and Cognitive Robotics Lab
Release Date:2018/09/25
Tag:Datenbank; Datenverarbeitung; Detektion; Digitale Bildverarbeitung; Kamera; Lernen; Materialveraenderung (allg); Neuronales Netz; Oberfläche; Straße; Straßennetz; Zustandsbewertung
Camera; Condition survey; Data base; Data processing; Detection; Deterioration; Image processing; Learning; Neural network; Road; Road network; Surface
Comment:
Außerdem beteiligt: Lehmann + Partner GmbH.
Volltext: DOI: 10.1109/IJCNN.2017.7966101
Source:2017 International Joint Conference on Neural Networks (IJCNN), S. 2039-2047
Institutes:Abteilung Straßenbautechnik / Abteilung Straßenbautechnik
Sonstige / Sonstige
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
collections:BASt-Beiträge / ITRD Sachgebiete / 61 Unterhaltung und Instandsetzung

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