FastRoadSeg: Fast Monocular Road Segmentation Network

Abstract

Monocular road segmentation is a fundamental component of autonomous driving. High accuracy has always been a focus of the community for this task. However, the exploration of high-speed methods is still lacking. This limits the prevalence of the monocular road segmentation method. In this paper, an efficient encoder-decoder network is proposed for segmenting the road from a single image. Specifically, to achieve high-speed inference, we leverage a shallow encoder for feature extraction and design a lightweight decoder for feature resolution recovery. To avoid the accuracy drop caused by network simplification, we introduce an efficient asymmetric dilated block to improve the ability of features to distinguish drivable/nondrivable roads to maintain the performance without excess computation. Experiments on three datasets verify the effectiveness of our method. On KITTI-Road, our approach ranks second among all monocular methods and even achieves a competitive performance compared to multimodal methods, with an inference speed over 135 FPS on a single TITAN Xp, which is at least 20 times faster than the state-of-the-art monocular method. Furthermore, our approach can be deployed on the embedded platform Jetson TX2 and achieves a speed of over 80 FPS. Codes will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gongshichina/FastRoadSeg</uri> .