Idioma:

Road surface detection and differentiation considering surface damages

Rateke, Thiago ; von Wangenheim, Aldo

Autonomous robots, 2021-02, Vol.45 (2), p.299-312 [Periódico revisado por pares]

New York: Springer US

Texto completo disponível

Citações Citado por

Enviar para

Título:
Road surface detection and differentiation considering surface damages
Autor: Rateke, Thiago ; von Wangenheim, Aldo
Assuntos: 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
É parte de: Autonomous robots, 2021-02, Vol.45 (2), p.299-312
Descrição: 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.
Editor: New York: Springer US
Idioma: Inglês

Voltar para lista de resultados

Resultado 1 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

Road surface detection and differentiation considering surface damages

Rateke, Thiago ; von Wangenheim, Aldo

New York: Springer US

Buscando em bases de dados remotas. Favor aguardar.