%0 Journal Article %A WANG Jia %A ZHANG Nan %A MENG Fanyun %A WANG Jinhe %T Improved Semantic Segmentation Algorithm Based on Pyramid Scene Parsing Network %D 2021 %R 10.3778/j.issn.1002-8331.2006-0366 %J Computer Engineering and Applications %P 220-227 %V 57 %N 19 %X

Image semantic segmentation is a classic problem in image recognition and a hot spot in machine vision research. However, in practical applications, there will be inaccurate semantic label prediction and edge information loss between the segmented object and the background, which has gradually become a bottleneck in image understanding. Accordingly, this paper proposes a network improvement structure based on the Pyramid Scene Parsing Network(PSPNet). Firstly, in the feature learning module, the input image is added to the original Residual Network(ResNet) by adding convolution and pooling operations within the network to further learn the features of each level, and add the multiple low-level feature maps learned to the high-level feature map to obtain a new feature map with more spatial location information. To obtain rich context information, it uses PSPNet’s pyramid pool structure, combining global context information in the feature map with local context information at different scales, convolution and upsampling to obtain the final prediction map. The simulation experimental results show that the improved method in the paper has a Mean Intersection over Union(MIoU) of 78.5% in the PASCAL VOC 2012 test set, which is 1.7% higher than the benchmark algorithm.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0366