Real-time Semantic Segmentation Method Based on Improved Feature Fusion - WANG Xiao yu; LI Zhi bin (Journal of Harbin University of Science and Technology, 2023)
Aiming at the problem that both location information and semantic information need to be considered in real-time semantic segmentation tasks, we proposed a real-time semantic segmentation method based on improved feature fusion. The method consists of a convolution neural network, a light attention module (light attention module, LAM) , and a bilateral feature fusion module (bilateral feature fusion module, BFFM). Firstly, quickly extract the location information and semantic information of the image by combining the convolutional neural network with the lightweight attention module. Then, the bilateral feature fusion module is used to guide the feature fusion of location information and semantic information. The results of the method on the data set of CamVid, mIoU reached 67. 8% , and the running speed reached 52. 6 fps. On the data set of Cityscapes, mIoU reached 73. 5% , and the running speed reached 31. 8 fps. The results show that the proposed segmentation method meets the accuracy and real-time requirements of segmentation, and can be applied to real-time semantic segmentation tasks.