基于注意力和多标签分类的图像实时语义分割

Real-Time Image Semantic Segmentation Based on Attention Mechanism and Multi-Label Classification

针对现阶段很多实时语义分割算法分割精度低,尤其对边界像素分割模糊的问题,提出一种基于跨级注意力机制和多标签分类的高精度实时语义分割算法.首先基于DeepLabv3进行优化,使其达到实时运算速度.然后在此网络基础上增加跨级注意力模块,使深层特征为浅层特征提供像素级注意力,以抑制浅层特征中不准确语义信息的输出;并在训练阶段引入多标签分类损失函数辅助监督训练.在Cityscapes数据集和CamVid数据集上的实验结果表明,该算法的分割精度分别为68.1%和74.1%,分割速度分别为42帧/s和89帧/s,在实时性与准确性之间达到较好的平衡,能够优化边缘分割,在复杂场景分割中具有较好的鲁棒性.

Improving the accuracy is the goal in real-time semantic segmentation,especially for fuzzy boundary pixel segmentation.We proposed a high-precision and real-time semantic segmentation algorithm based on cross-level attention mechanism and multi-label classification.The procedure started with an optimization of DeepLabv3 to achieve real-time segmentation speed.Then,a cross-level attention module was added,so that the high-level features provided pixel-level attention for the low-level features,so as to inhibit the output of inaccurate semantic information in the low-level features.In the training phase,the multi-label classification loss function was introduced to assist the supervised training.The experimental results on Cityscapes dataset and CamVid dataset show that the segmentation accuracy is 68.1%and 74.1%respectively,and the segmentation speed is 42 frames/s and 89 frames/s respectively.It achieves a good balance between segmentation speed and accuracy,can optimize edge segmentation,and has strong robustness in complex scene segmentation.