In order to balance accuracy and real-time performance in semantic segmentation, this paper proposes a real-time semantic segmentation algorithm model based on attention mechanism and multi-branch feature fusion using Fast convolutional neural network model (Fast-SCNN). In this method, the spatial detail feature enhancement branch is introduced to enhance spatial detail features firstly. Then, through rational design of fusion module, the feature information of each branch is optimized to achieve better fusion of deep and shallow features. At the end of the feature fusion module, an adaptive feature enhancement focus module is introduced to capture the interdependence between remote pixels. The experimental results show that the proposed algorithm achieves 71.55% segmentation accuracy on Cityscapes dataset, the reasoning speed FPS is 97.6 frames/s, and the number of parameters is 1.39 M, which verifies the effectiveness of the network model constructed by the algorithm. Code is available at https://github.com/ccchhheeennn/model.

中文翻译：

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AM-MulFSNet：结合注意力机制和多分支的快速语义分割网络

为了平衡语义分割的准确性和实时性，本文利用快速卷积神经网络模型（Fast-SCNN）提出一种基于注意力机制和多分支特征融合的实时语义分割算法模型。该方法首先引入空间细节特征增强分支来增强空间细节特征。然后，通过融合模块的合理设计，对各个分支的特征信息进行优化，实现深层和浅层特征更好的融合。在特征融合模块的最后，引入自适应特征增强聚焦模块来捕获远程像素之间的相互依赖性。实验结果表明，该算法在Cityscapes数据集上实现了71.55%的分割准确率，推理速度FPS为97.6帧/s，参数数量为1.39 M，验证了该算法构建的网络模型的有效性。代码可在 https://github.com/ccchhheeennn/model 获取。