RoadSegNet: a deep learning framework for autonomous urban road detection

Abstract

Abstract Ground detection is an essential part of the perception system in self-driving cars. The ground can be imagined as a fairly smooth, drivable area that is even textured and easily distinguished from the surrounding area. It can have some common imperfections, like shadows and differing light intensities. In this paper, a comparative study of several deep neural network architectures has been reported that can deduce surface normal information on the classic KITTI road dataset in various challenging scenarios. Our goal is to simplify the task of how the recent methods perceive the ground-related information and propose a solution by testing it on three state-of-the-art deep learning models, which are “Resnet-50,” “Xception,” and “MobileNet-V2” to understand and exploit the capabilities of these models. The main significance of this comparative study has been to evaluate the performance of these networks for edge deployment. So, the tiny DNN model of MobileNet-V2 has been considered, which has approximately 80% fewer tunable parameters as compared to the others. The obtained results show that the proposed networks are able to achieve a segmentation accuracy of more than ~ 96% and that too in various challenging scenarios.

Keywords

• Autonomous driving
• Driver assistance system
• Semantic segmentation
• Deep learning