Semantic segmentation of urban street scene based on DeepLabv3+

Haihong Yang; Long Yan; Xingfang Zhao

doi:10.1117/12.2656819

20 October 2022 Semantic segmentation of urban street scene based on DeepLabv3+

Haihong Yang, Long Yan, Xingfang Zhao

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124513N (2022) https://doi.org/10.1117/12.2656819
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

The inability to extract and utilize features in semantic segmentation leads to the loss of detailed information and the discontinuity of semantic information, and this study proposed an encoding and decoding model CAF-Deeplabv3+ for semantic segmentation based on coordinate attention and feature fusion. First, the coordinate attention module (CA) is embedded in the backbone network ResNet for the feature extraction; Secondly, a strip pooling branch (SP) is added to the ASPP module in encoding structure to help capture contextual information; In the process of feature reuse, a feature fusion module (FFM) for fusing low-level features and high-level features. It has been verified that the model can enhance the feature extraction capabilities. Our method achieved excellent results on Cityscapes. The training and validation on the Cityscapes dataset showed that the experimental results achieve 75.01% of the mean intersection over union (mIOU), which is improved by nearly 2%.

Citation Download Citation

Haihong Yang, Long Yan, and Xingfang Zhao "Semantic segmentation of urban street scene based on DeepLabv3+", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124513N (20 October 2022); https://doi.org/10.1117/12.2656819

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KEYWORDS

Image segmentation

Convolution

Computer programming

Surface plasmons

Feature extraction

Image fusion

Network architectures

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