TY  - CONF
A1  - Eisenbach, Markus
A1  - Stricker, Ronny
A1  - Seichter, Daniel
A1  - Amende, Karl
A1  - Debes, Klaus
A1  - Sesselmann, Maximilian
A1  - Ebersbach, Dirk
A1  - Stöckert, Ulrike
A1  - Gross, Horst Michael
T1  - How to get pavement distress detection ready for deep learning? A systematic approach
N2  - 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.
KW  - Datenbank
KW  - Datenverarbeitung
KW  - Detektion
KW  - Digitale Bildverarbeitung
KW  - Kamera
KW  - Lernen
KW  - Materialveraenderung (allg)
KW  - Neuronales Netz
KW  - Oberfläche
KW  - Straße
KW  - Straßennetz
KW  - Zustandsbewertung
KW  - Camera
KW  - Condition survey
KW  - Data base
KW  - Data processing
KW  - Detection
KW  - Deterioration
KW  - Image processing
KW  - Learning
KW  - Neural network
KW  - Road
KW  - Road network
KW  - Surface
Y1  - 2017
UR  - https://bast.opus.hbz-nrw.de/frontdoor/index/index/docId/1993
N1  - Außerdem beteiligt: Lehmann + Partner GmbH.
Volltext: DOI: 10.1109/IJCNN.2017.7966101
ER  -