Aerial Remote Sensing Image Cascaded Road Detection Network Based on Edge Sensing Module and Attention Module

Abstract

Automatic road detection has always been an important research problem in the field of remote sensing image processing, which is of great significance for many applications. However, many road areas cannot be effectively detected and the existing road detection methods suffer from unsmooth edges due to shadow phenomena and the occlusion of objects such as trees. For this reason, we propose a cascaded automatic road detection network based on edge sensing module and attention module, called CasEANet. CasEANet consists of three tasks including road surface detection, road edge detection and road centerline extraction. An encoder-decoder structure integrating edge sensing module and attention module is applied to detect the road surface. In order to obtain smoother road edges, the edge sensing module is designed to enhance the perception of road edges. The attention module is employed to guide the network to reinforce the perception of global information, aiming to solve the problem of discontinuity in the detection of road surfaces that are obscured by trees. The centerline extraction is adopted to assist the training of road surface detection. Experiments performed on the RNBD dataset prove the effectiveness of CasEANet. Specially, the F1 score, overall accuracy and balanced error rates of the CasEANet are 0.946, 0.986 and 0.0219 respectively, outperform the other state-of-the-art methods. The code will release soon on https://github.com/HITLDY/CasEANet.