Lightweight Image Semantic Segmentation Based on Attention Mechanism and Densely Adjacent Prediction
A novel algorithm named as lightweight image semantic segmentation based on attention mechanism and densely adja-cent prediction is proposed to avoid the disadvantages of the difficulty in highlighting important channel features for atrous spatial pyramid pooling module,higher computational complexity and lacking of sufficient detailed information for the high level semantic feature map generated by the decoder in DeepLabv3+algorithm.The lightweight MobileNetV2 is regarded as the backbone net-work to reduce model parameters.After the multi-scale information is extracted by the channel atrous spatial pyramid pooling,each channel of the feature map is weighted to reinforce the learning of important channel features.Moreover,the segmentation results are refined since densely adjacent prediction is utilized to combine high-level and low-level features.Experiments are per-formed on the PASCAL VOC 2012 augmented dataset,and the experimental results show that both mean Intersection over union and mean pixel accuracy of the proposed method are higher than the state-of-the-art algorithms.Compared with DeepLabv3+,the parameters and calculation amount are decreased by 184.82 ×106 and 90.83GFLOPs respectively.The proposed algorithm not only improves the segmentation accuracy,but also reduces the computation cost compared to the baseline algorithm.
Deep learningSemantic segmentationDeepLabv3+Attention mechanism
NETL
NSTL
万方数据
