Vision-based Road Segmentation for Intelligent Vehicles using Deep Convolutional Neural Networks

Abstract

Intelligent Transportation Systems are crucial to enhance traffic security and reduce traffic violations. Recent developments in artificial intelligence have also motivated researchers to work on these systems. In order to reduce the risk of road accidents, accurately determining the driving area is important. On the other hand, automated road segmentation is challenging because of different weather, light, road conditions, etc. Also, the roads’ design characteristics may be different and may include very small and large features. In this paper, a vision-based road segmentation system is presented. To apply this system, a CNN architecture based on consecutive triple filter size has been designed. The consecutive triple filter size approach avoids missing both small and large features. Proposed CNN architecture consists of an encoder and a decoder subnetwork. Preliminary experimental results achieved 95.84% IoU, 97.86% TPR, and 2.34% FPR indexes for road segmentation tasks on the CamVid database. This study presents a low-cost, reliable, and basic road segmentation system for intelligent vehicles. Also, it is a step toward a more complex system to alert the driver about dangerous situations. Owing to the proposed system, a successful road segmentation is possible using a simple camera.