Vision-Based Road Detection using Contextual Blocks

TL;DR

This paper introduces the use of contextual blocks to improve monocular road detection in autonomous systems, enhancing accuracy and efficiency while maintaining competitive benchmark performance.

Contribution

It proposes a novel use of contextual blocks for better feature integration in road detection, with an emphasis on computational efficiency and practical implementation.

Findings

Significant improvement from contextual blocks.

Achieved competitive results on KITTI benchmark.

Demonstrated computational efficiency of the approach.

Abstract

Road detection is a fundamental task in autonomous navigation systems. In this paper, we consider the case of monocular road detection, where images are segmented into road and non-road regions. Our starting point is the well-known machine learning approach, in which a classifier is trained to distinguish road and non-road regions based on hand-labeled images. We proceed by introducing the use of "contextual blocks" as an efficient way of providing contextual information to the classifier. Overall, the proposed methodology, including its image feature selection and classifier, was conceived with computational cost in mind, leaving room for optimized implementations. Regarding experiments, we perform a sensible evaluation of each phase and feature subset that composes our system. The results show a great benefit from using contextual blocks and demonstrate their computational efficiency. Finally, we submit our results to the KITTI road detection benchmark achieving scores comparable with state of the art methods.