Deep Dual-Resolution Road Scene Segmentation Networks Based on Decoupled Dynamic Filter and Squeeze–Excitation Module

Abstract

Image semantic segmentation is an important part of automatic driving assistance technology. The complexity of road scenes and the real-time requirements of application scenes for segmentation algorithm are the challenges facing segmentation algorithms. In order to meet the above challenges, Deep Dual-resolution Road Scene Segmentation Networks based on Decoupled Dynamic Filter and Squeeze–Excitation (DDF&SE-DDRNet) are proposed in this paper. The proposed DDF&SE-DDRNet uses decoupled dynamic filter in each module to reduce the number of network parameters and enable the network to dynamically adjust the weight of each convolution kernel. We add the Squeeze-and-Excitation module to each module of DDF&SE-DDRNet so that the local feature map in the network can obtain global features to reduce the impact of image local interference on the segmentation result. The experimental results on the Cityscapes dataset show that the segmentation accuracy of DDF&SE-DDRNet is at least 2% higher than that of existing algorithms. Moreover, DDF&SE-DDRNet also has satisfactory inferring speed.