Research on Improved Feature Pyramid Algorithm Integrating Border Supervision Strategy

Authors

Hong Sun, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;Follow
Yuelan Ling, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;Follow
Yuxiang Zhang, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: Aiming at the inaccurate boundary division in semantic segmentation and the existence of multi-scale targets, an improved feature pyramid algorithm fused with boundary supervision strategies is proposed. By fusing the boundary supervision strategy and the improved feature pyramid algorithm, the problems of inaccurate boundary division and the existence of multi-scale targets are sloved respectively, and an attention mechanism is added in the upsampling process to further improve the segmentation effect. The experimental results show that the algorithm can reach 58.69% and 78.59% MIOU (mean intersection over union) indicators on the Camvid and PASCAL VOC2012 data sets respectively, and has a good performance in the segmentation effect.

Recommended Citation

Sun, Hong; Ling, Yuelan; and Zhang, Yuxiang (2022) "Research on Improved Feature Pyramid Algorithm Integrating Border Supervision Strategy," Journal of System Simulation: Vol. 34: Iss. 10, Article 2.
DOI: 10.16182/j.issn1004731x.joss.21-0529
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss10/2

First Page

2119

Revised Date

2021-08-11

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0529

Last Page

2129

CLC

TP391

Recommended Citation

Hong Sun, Yuelan Ling, Yuxiang Zhang. Research on Improved Feature Pyramid Algorithm Integrating Border Supervision Strategy[J]. Journal of System Simulation, 2022, 34(10): 2119-2129.

Corresponding Author

Yuelan Ling,17701603738@163.com

DOI

10.16182/j.issn1004731x.joss.21-0529