A Lightweight Road Detection Algorithm Based on Multiscale Convolutional Attention Network and Coupled Decoder Head

Abstract

Automatic road detection from remote sensing images has always been a significant research topic. It is of great value to many practical applications. However, there are still some problems need to be solved. First of all, most of existing road detection methods are inefficient because of the sequential processing of the decoder head. Secondly, some existing methods are unable to detect occluded road areas effectively. For this reason, we focus on the speed and occlusion problems in road detection network, and propose a new lightweight road detection method based on multi-scale convolution attention network (MSCAN) and coupled decoder head, LRDNet. In particular, LRDNet adopt multi-scale convolution attention network with large receptive field for feature extraction to solve the occlusion problem, and decode the road surface, road edge and road centerline in a coupled way to improve the speed of road detection and ensure that the road surface detection results have fewer burrs at the road edge. We have performed several experiments on the RNBD dataset. Compared with some state-of-the-art methods, the experimental results prove the validity of the proposed LRDNet. The code will be released soon on the site of https://github.com/dyl96/LRDNet.