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

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.