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Road surface detection and differentiation considering surface damages

Autonomous robots, 2021-02, Vol.45 (2), p.299-312 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. ;ISSN: 0929-5593 ;EISSN: 1573-7527 ;DOI: 10.1007/s10514-020-09964-3

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Title:
Road surface detection and differentiation considering surface damages
Author: Rateke, Thiago ; von Wangenheim, Aldo
Subjects: Artificial Intelligence ; Artificial neural networks ; Computer Imaging ; Control ; Damage detection ; Engineering ; Ground truth ; Image segmentation ; Mechatronics ; Obstacle avoidance ; Original Research ; Pattern Recognition and Graphics ; Road surface ; Roads & highways ; Robotics ; Robotics and Automation ; Unpaved roads ; Vehicle safety ; Vision ; Visual fields ; Visual perception
Is Part Of: Autonomous robots, 2021-02, Vol.45 (2), p.299-312
Description: A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surfaces that may be relevant to driving safety. Our approach makes use of Convolutional Neural Networks (CNN) to perform semantic segmentation, we use the U-NET architecture with ResNet34, in addition we use the technique known as Transfer Learning, where we first train a CNN model without using weights in the classes as a basis for a second CNN model where we use weights for each class. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.
Publisher: New York: Springer US
Language: English
Identifier: ISSN: 0929-5593
EISSN: 1573-7527
DOI: 10.1007/s10514-020-09964-3
Source: ProQuest Central

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Road surface detection and differentiation considering surface damages

The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. ;ISSN: 0929-5593 ;EISSN: 1573-7527 ;DOI: 10.1007/s10514-020-09964-3

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