Abstract
Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set.
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