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 -