Standard
Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN. /
Kortmann, Felix; Talits, Kevin; Fassmeyer, Pascal et al.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020: Proceedings, Dec 10 - Dec 13, 2020 • Virtual Event. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. p. 5563-5571 9378245 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Kortmann, F, Talits, K, Fassmeyer, P, Warnecke, A
, Meier, N, Heger, J, Drews, P & Funk, B 2020,
Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds),
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020: Proceedings, Dec 10 - Dec 13, 2020 • Virtual Event., 9378245, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 5563-5571, IEEE International Conference on Big Data - BigData2020, Atlanta, United States,
10.12.20.
https://doi.org/10.1109/BigData50022.2020.9378245
APA
Kortmann, F., Talits, K., Fassmeyer, P., Warnecke, A.
, Meier, N., Heger, J., Drews, P., & Funk, B. (2020).
Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.),
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020: Proceedings, Dec 10 - Dec 13, 2020 • Virtual Event (pp. 5563-5571). Article 9378245 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). IEEE - Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/BigData50022.2020.9378245
Vancouver
Kortmann F, Talits K, Fassmeyer P, Warnecke A
, Meier N, Heger J et al.
Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020: Proceedings, Dec 10 - Dec 13, 2020 • Virtual Event. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2020. p. 5563-5571. 9378245. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378245
Bibtex
@inbook{286c5afaa6fa4515baab29e9f431f0ce,
title = "Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN",
abstract = "Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the {\textquoteright}IEEE BigData Cup Challenge{\textquoteright} and its dataset {\textquoteright}RDD-2020{\textquoteright} with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.",
keywords = "Business informatics",
author = "Felix Kortmann and Kevin Talits and Pascal Fassmeyer and Alexander Warnecke and Nicolas Meier and Jens Heger and Paul Drews and Burkhardt Funk",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9378245",
language = "English",
isbn = "978-1-7281-6252-2",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "5563--5571",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
address = "United States",
note = "IEEE International Conference on Big Data - BigData2020, BigData2020 ; Conference date: 10-12-2020 Through 13-12-2020",
url = "https://bigdataieee.org/BigData2020/",
}
RIS
TY - CHAP
T1 - Detecting Various Road Damage Types in Global Countries Utilizing Faster R-CNN
AU - Kortmann, Felix
AU - Talits, Kevin
AU - Fassmeyer, Pascal
AU - Warnecke, Alexander
AU - Meier, Nicolas
AU - Heger, Jens
AU - Drews, Paul
AU - Funk, Burkhardt
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.
AB - Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85103859214&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378245
DO - 10.1109/BigData50022.2020.9378245
M3 - Article in conference proceedings
SN - 978-1-7281-6252-2
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 5563
EP - 5571
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - IEEE International Conference on Big Data - BigData2020
Y2 - 10 December 2020 through 13 December 2020
ER -
