Abstract: Feature fusion in real-time semantic segmentation needs to pay attention to both shallow and deep information， while the current feature fusion methods require a huge amount of computation and parameter count， which is difficult to meet the requirements of real-time semantic segmentation in terms of accuracy and speed. To address this problem， a real-time semantic segmentation method based on gated fusion is proposed from the comprehensive consideration of both real-time and performance of the network. The method contains an encoder， a gated feature fusion module， a pixel-level feature extraction module， and a gated aggregation segmentation head. Firstly， the image to be segmented is feature extracted by the encoder. Secondly， the important feature information is accurately extracted by the pixel-level feature extraction module， then the deep semantic information and the shallow location information are feature fused by the gated feature fusion module. Finally the semantic segmentation is completed by the gated aggregation segmentation head. On the dataset CamVid， the mean intersection over union of the model segmentation is 87.31%， and the frame rate of segmentation is 75.3 fps. On the dataset Cityscapes， the mean intersection over union of the model segmentation is 79.19%， and the frame rate of segmentation is 44.1 fps. Experimental results show that the proposed segmentation method performs well in both accuracy and real-time， and it can be effectively applied to real-time semantic segmentation tasks.

Key words: real-time semantic segmentation, feature fusion, image processing, image semantic segmentation