Research on semantic segmentation technology based on attention mechanism and residual learning

Tao Liu; Yan Piao; Denghui Qin

doi:10.1117/12.2641285

4 August 2022 Research on semantic segmentation technology based on attention mechanism and residual learning

Tao Liu, Yan Piao, Denghui Qin

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 1230620 (2022) https://doi.org/10.1117/12.2641285
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

Aiming at the problem that the image edge is blurred and the middle hidden layer features are lost in image semantic segmentation, the attention mechanism of joint training in channel domain and spatial domain is proposed, and the multiobjective joint training loss function and residual connection module are used to learn the semantic features in semantic segmentation, and the hidden layer features in the process of network training are added to the calculation of loss value. The experimental results show that the introduction of multi-channel attention mechanism and residual connection in semantic segmentation network is helpful to improve the effect of semantic segmentation. Pascal voc2012 data set was used in the experiment.

Citation Download Citation

Tao Liu, Yan Piao, and Denghui Qin "Research on semantic segmentation technology based on attention mechanism and residual learning", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 1230620 (4 August 2022); https://doi.org/10.1117/12.2641285

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KEYWORDS

Image segmentation

Feature extraction

Image processing

Image fusion

Convolution

Neural networks

Electronics engineering

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