Optimization of Image Semantic Segmentation Algorithms Based on Deeplab v3+

Herein, a new semantic segmentation model N-Deeplab v3+ was proposed based on the existing Deeplab v3+ algorithm. The proposed model can be used to address some severe problems of Deeplab v3+ related to the loss of details, such as missing and incorrect segmentations, during image semantic segmentation. The new model designed an atrous spatial pyramid pooling structure with heteroreceptive field splicing to enhance the correlation between different-level data. The feature fusion of multiple crosslayers is performed to improve the characterization of image details. A feature alignment module based on the attention mechanism was developed to guide the alignment of high- and low-level features and enhance the learning process for important channel features in a targeted manner, thus improving the learning ability of the model. Experimental results based on the Cityscapes dataset show that the proposed model can effectively increase the attention for small-scale targets, alleviate the problem of target mis-segmentation, and show improved semantic segmentation accuracy. The generalization capability of the proposed model is further verified on the PASCAL VOC 2012 dataset. The mean intersection over union of N-Deeplab v3+ on the Cityscapes dataset and PASCAL VOC 2012 dataset reaches 76. 31% and 81. 97%, respectively, showing improvements of 1. 69 percentage points and 2. 14 percentage points, respectively, compared with the original model.