Importance of Contextual Information for the Detection of Road Damages

In this paper we give an overview on methods for the optical detection of road surface damages and analyze the importance of contextual information. The objective is to improve the optical detection of road damages, especially potholes, based on images from windscreen mounted monocular cameras, as well as to reduce the complexity and thus save computing capacity. In order to achieve this, parts of the image that are not classified as road are preprocessed. Thus two different parts were implemented and analyzed: (i) a semantic segmentation for classifying the contextual information and (ii) a pothole detection.First, different semantic segmentation networks are compared by varying the training dataset and the number of predicted classes to find a trade-off between accuracy and the level of detail of the provided information. In the second step, this segmentation into road and other is used to evaluate the importance of the contextual information in the detection part. The detection accuracy is compared using varying input datasets by hiding and adjusting the non road parts. From the state of the art it can be derived that the elimination of contextual information reduces the complexity for the detection of road surface damages like potholes especially for computer vision (CV) based approaches. In addition, our results show that deep learning (DL) approaches need at least a simplified contextual information.

[1]  L. Vogel,et al.  A relationship between accident types and causes , 2005 .

[2]  Christoph Ament,et al.  Accuracy and robustness of road observers with uncertainties for reconstruction of the road elevation profile , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[3]  Taehyeong Kim,et al.  Review and Analysis of Pothole Detection Methods , 2014 .

[4]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[5]  Marthinus J. Booysen,et al.  Detecting potholes using simple image processing techniques and real-world footage , 2015 .

[6]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Aniket Kulkarni,et al.  Pothole Detection System using Machine Learning on Android , 2014 .

[8]  Detlef Kuck,et al.  Electronic Horizon - Providing Digital Map Data for ADAS Applications , 2016 .

[9]  Emir Buza,et al.  Pothole detection: An efficient vision based method using RGB color space image segmentation , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[10]  Amir Golroo,et al.  A review on automated pavement distress detection methods , 2017 .

[11]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[12]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[14]  Peter Kontschieder,et al.  The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Ning Zhang,et al.  Road Damage Detection Using RetinaNet , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[16]  Yung-Cheol Byun,et al.  Pothole Detection using Machine Learning , 2018 .

[17]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[18]  Ruigang Yang,et al.  The ApolloScape Open Dataset for Autonomous Driving and Its Application , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  J. Zico Kolter,et al.  Intelligent Pothole Detection and Road Condition Assessment , 2017, ArXiv.

[20]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[21]  Vaibhav Darbari,et al.  Crack-pot: Autonomous Road Crack and Pothole Detection , 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).

[22]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[23]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.