EACNet: Enhanced Asymmetric Convolution for Real-Time Semantic Segmentation
Yaqian Li, Xiaokun Li, Cunjun Xiao, Haibin Li, Wenming Zhang
Abstract
Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can't meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise asymmetric convolution and dilated convolution to extract short-range and long-range features, which are efficient and powerful. Additionally, we apply a bilateral structure in which the detail branch preserves low-level spatial details while the semantic branch captures high-level context information. The two branches are merged at different stages of the network to strengthen information propagation between different levels. The experiments on the Cityscapes dataset show that our method achieves high accuracy and speed with relatively small parameters. Compared with other real-time semantic segmentation methods, our network attains a good trade-off among parameters, speed, and accuracy.