| Title: | OS6-2 Research on Improved PPLCNet Classification Network Based on CBAM Attention Mode |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 151-155 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS6-2 |
| Author(s): | Peng Wang, Shengfeng Wang, Qikun Wang, Yuting Zhou |
| Publication Date: | February 13, 2025 |
| Keywords: | Neural networks, Pedestrian attributes, CBAM, Deep learning |
| Abstract: | This paper studies pedestrian attribute recognition based on the pplcnet network because it is of great significance in the field of traffic security. Firstly, the research status of pedestrian attribute recognition and common deep learning models are introduced. Secondly, considering that convolutional block attention module contains both spatial attention module and channel attention module, we add this attention model to pplcnet to improve performance. Finally, this paper verifies the model through the pa100k dataset and obtains good results. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS6/OS6-2.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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