Title: Convolutional neural networks for obstacle detection on the road and driving assistance

Authors: Ramzi Mosbah; Larbi Guezouli

Addresses: Department of Computer Science, University of Batna 2, Batna, Algeria ' LaSTIC Laboratory, Department of Computer Science, University of Batna 2, Batna, Algeria

Abstract: Generally, a driver has moments of inattention, that can cause considerable damage. To deal with this issue, we have to detect obstacles on the road automatically. To do that, several challenges appear. Firstly, we have to locate the region of interest, which is the road part in the frame, then we have to detect the objects inside the region of interest. In this work we propose an improved driver assistance system using a camera on the front of the car. Acquired images from this camera feed our system. In the frames to be processed, we reduce the region of interest to the area of the road. Obstacles on the road are sought in this region of interest. At the same time, we take care of the driver by detecting whether he is drowsy. Experimental results were evaluated using KITTI Vision Benchmark Suite and short videos recorded on streets in Batna.

Keywords: obstacle detection; image edge detection; driving assistance; object recognition; convolutional neural networks.

DOI: 10.1504/IJCVR.2022.126504

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.6, pp.573 - 594

Received: 09 Feb 2021
Accepted: 10 Sep 2021
Published online: 27 Oct 2022 *

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