Image Semantic Segmentation Algorithm Based on Improved DeepLabv3+

Authors

Weiping Zhao, Liaoning General Aviation Academy, Shenyang Aerospace University, Shenyang 110034, China; College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110034, ChinaFollow
Yu Chen, College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110034, ChinaFollow
Song Xiang, Liaoning General Aviation Academy, Shenyang Aerospace University, Shenyang 110034, China
Yuanqiang Liu, Liaoning General Aviation Academy, Shenyang Aerospace University, Shenyang 110034, China
Chaoyue Wang, Liaoning General Aviation Academy, Shenyang Aerospace University, Shenyang 110034, China

Abstract

Abstract: Mainstream image semantic segmentation networks currently face problems such as incorrec segmentation, discontinuous segmentation, and high model complexity, which cannot be flexibly and efficiently deployed in practical scenarios. To this end, an image semantic segmentation network that optimizes the DeepLabv3+ model is designed by comprehensively considering the network parameters, prediction time, and accuracy. The lightweight EfficientNetv2 is adopted to extract backbone network features and improve parameter utilization. In the atrous spatial pyramid pooling module, the mixed strip pooling is utilized to replace the global average pooling, and a depthwise separable dilated convolution is introduced to reduce parameters and improve the ability to learn multi-scale information. The attention mechanism is employed to enhance the model's representation power, and the multiple shallow features of the backbone network are extracted to enrich the image's geometric details. The experiment shows that the algorithm achieves 81.19% mIoU with a parameter size of 55.51×106, which optimizes the segmentation accuracy and model complexity and improves model generalization.

Recommended Citation

Zhao, Weiping; Chen, Yu; Xiang, Song; Liu, Yuanqiang; and Wang, Chaoyue (2023) "Image Semantic Segmentation Algorithm Based on Improved DeepLabv3+," Journal of System Simulation: Vol. 35: Iss. 11, Article 4.
DOI: 10.16182/j.issn1004731x.joss.22-0690
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss11/4

First Page

2333

Last Page

2344

CLC

TP391

Recommended Citation

Zhao Weiping, Chen Yu, Xiang Song, et al. Image Semantic Segmentation Algorithm Based on Improved DeepLabv3+[J]. Journal of System Simulation, 2023, 35(11): 2333-2344.

Corresponding Author

Chen Yu

DOI

10.16182/j.issn1004731x.joss.22-0690