Gated Multi-Layer Fusion for Real-Time Semantic Segmentation

In order to solve the problem that it is difficult to recover the pixels precisely in upsampling because of the loss of pixels caused by downsampling,a real-time semantic segmentation method based on gated multi-layer fusion is proposed.Considering the real-time of segmentation,we use the light model as the basic network to extract feature information.To improve the recovering accuracy of pixels,a gated attention structure is designed to horizontally connect the downsample lay to upsample lay,which can filter the features of object,and enhance the information diversity of the upsampling feature map.In addition,a multi-layer fusion structure is proposed to integrate the semantic information of different network layers,and is used to supplement the missing pixels by utilizing the semantic differences between different layers.The experimental results on CamVid and VOC datasets show that the segmentation accuracy of the proposed model is 72.9%,and the average segmentation speed is 43.1 frames per second,where the input image size of the network is 512×512.