MPE-HRNetL: A Lightweight High-Resolution Network for Multispecies Animal Pose Estimation

被引:0
|
作者
Shen, Jiquan [1 ,2 ]
Jiang, Yaning [3 ]
Luo, Junwei [1 ]
Wang, Wei [4 ]
机构
[1] Henan Polytech Univ, Sch Software, Jiaozuo 454000, Peoples R China
[2] Anyang Inst Technol, Anyang 455000, Peoples R China
[3] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Peoples R China
[4] CSSC Haifeng Aviat Technol Co Ltd, 4,Xinghuo Rd,Fengtai Sci Pk, Beijing 100070, Peoples R China
基金
中国国家自然科学基金;
关键词
animal pose estimation; multiscale mixed attention mechanism; high-resolution network; spatial pyramid pooling; Lite-HRNet; BEHAVIOR;
D O I
10.3390/s24216882
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Animal pose estimation is crucial for animal health assessment, species protection, and behavior analysis. It is an inevitable and unstoppable trend to apply deep learning to animal pose estimation. In many practical application scenarios, pose estimation models must be deployed on edge devices with limited resource. Therefore, it is essential to strike a balance between model complexity and accuracy. To address this issue, we propose a lightweight network model, i.e., MPE-HRNet.L, by improving Lite-HRNet. The improvements are threefold. Firstly, we improve Spatial Pyramid Pooling-Fast and apply it and the improved version to different branches. Secondly, we construct a feature extraction module based on a mixed pooling module and a dual spatial and channel attention mechanism, and take the feature extraction module as the basic module of MPE-HRNet.L. Thirdly, we introduce a feature enhancement stage to enhance important features. The experimental results on the AP-10K dataset and the Animal Pose dataset verify the effectiveness and efficiency of MPE-HRNet.L.
引用
收藏
页数:16
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