Real-time semantic segmentation based on improved BiSeNet

To improve the performance of image semantic segmentation on accuracy and efficiency for practical applications, in this study, we propose a real-time semantic segmentation algorithm based on improved BiSeNet. First, the redundancy of certain channels and parameters of BiSeNet is eliminated by sharing the heads of dual branches, and the affluent shallow features are effectively extracted at the same time. Subsequently, the shared layers are divided into dual branches, namely, the detail branch and the semantic branch, which are used to extract detailed spatial information and contextual semantic information, respectively. Furthermore, both the channel attention mechanism and spatial attention mechanism are introduced into the tail of the semantic branch to enhance the feature representation; thus the BiSeNet is optimized by using dual attention mechanisms to extract contextual semantic features more effectively. Finally, the features of the detail branch and semantic branch are fused and up-sampled to the resolution of the input image to obtain semantic segmentation. Our proposed algorithm achieves 77. 2% mIoU on accuracy with real-time performance of 95. 3 FPS on Cityscapes dataset and 73. 8% mIoU on accuracy with real-time performance of 179. 1 FPS on CamVid dataset. The experiments demonstrate that our proposed semantic segmentation algorithm achieves a good trade-off between accuracy and efficiency. Furthermore, the performance of semantic segmentation is significantly improved compared with BiSeNet and other existing algorithms. © 2023 Chinese Academy of Sciences. All rights reserved.