Research on Scene Semantic Segmentation Based on Deep Learning
Authors: 
Kun Li, Liwei Jia, Xiaoming ShiAuthors Info & Claims
Kun Li, Liwei Jia, Xiaoming ShiAuthors Info & ClaimsPages 1 - 5
Abstract
Because of the problem of the low accuracy and slow speed of the traditional semantic segmentation model, making it difficult to actually use. In response to this problem, this paper focuses on the method to improve the precision and speed of the algorithm. According to this theory, based on the convolution neural network, we have designed the PSPNet and ICNet models. Meanwhile, a scene semantic segmentation network based on deep learning was presented. The network effectively improves the accuracy of semantic segmentation of convolutional neural networks by merging multi-level depth and network features. The test results on the LISA traffic sign data set show that the proposed semantic segmentation network has outstanding performance compared with other state of the art semantic segmentation network structures.
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Index Terms
- Research on Scene Semantic Segmentation Based on Deep Learning
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Published In
October 2020
527 pages
ISBN:9781450387729
DOI:10.1145/3419635
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Association for Computing Machinery
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Published: 18 September 2020
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CIPAE 2020
CIPAE 2020: 2020 International Conference on Computers, Information Processing and Advanced Education
October 16 - 18, 2020
ON, Ottawa, Canada
Acceptance Rates
CIPAE 2020 Paper Acceptance Rate 101 of 216 submissions, 47%;
Overall Acceptance Rate 101 of 216 submissions, 47%
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