LightSeg: a light-weight network for real-time semantic segmentation

Abstract

Semantic segmentation is an intensive prediction task with a wide range of applications, such as mobile phones, robots, autonomous driving and other mobile devices. The computing power of these mobile devices is often very limited, which limits the application of semantic segmentation. This paper designs a simple and efficient end-to-end real-time segmentation algorithm. Our algorithm model is an asymmetric encoder-decoder structure, which is composed of two parts: the first part is a lightweight encoder, which consists of a dual-path fast downsampling module and a lightweight three-stage feature extractor; the second part is an asymmetric decoding classifier, which consists of a mutual attention feature fusion module, a lightweight atrous spatial pyramid pooling module, and a classifier. Our method has only 0.64M parameters, and achieves 68.4% mIoU, 67.5 FPS with an image of 1024\uf0cd2048 resolutions, also reachs 203.8 FPS, 61.9% mIoU with 512\uf0cd1024 resolutions, on the Cityscapes test set on GTX1080Ti. Comprehensive experiments show that our method can reach the state-of-the-art level on the Cityscapes dataset in terms of accuracy and speed.