计算机工程与应用2025，Vol.61Issue(6)：282-294,13.DOI:10.3778/j.issn.1002-8331.2311-0057
融合空间与通道重构卷积和注意力的轻量型动物姿态估计
Lightweight Animal Pose Estimation with Integrated Spatial and Channel Reconstructive Con-volutions and Attention
摘要
Abstract
The importance of animal pose estimation in fields such as behavioral ecology,animal health monitoring,and wildlife conservation has been increasingly emphasized.However,current mainstream algorithms for animal pose estima-tion tend to prioritize accuracy,leading to a continuous increase in network complexity and computational cost,which limits their application on mobile devices and embedded platforms.In response to this issue,this paper proposes a multi-scale animal pose estimation network called SPANet,which combines spatial and channel-reconstructing convolutions with pyramid split attention.Firstly,the bottleneck layer EPSAneck of the high-resolution network is redesigned by incor-porating pyramid split attention and coordinate attention mechanisms.This redesign not only reduces the computational cost caused by excessive use of large convolutional kernels but also enhances the ability of network to extract useful features.Secondly,the SCCAblock foundational module is introduced,which is based on spatial and channel-reconstructing convolutions as well as coordinate attention mechanisms.This module significantly reduces computational redundancy and memory access while enhancing information exchange between channels and spatial dimensions.Lastly,the fusion method of network output features is re-designed using deconvolution modules to further improve the accuracy of the network.Experimental results demonstrate that compared to the high-resolution network,the proposed network model achieves an average precision improvement of 1.8 percentage points on the AP10K test set,while reducing the floating-point operations by 48.5%and the number of model parameters by 67.0%.On the AnimalPose dataset,the floating-point operations are reduced by 49.5%,and the number of model parameters is reduced by 67.0%.The experimental data indi-cate that the proposed network model achieves a small-range improvement in prediction accuracy while reducing the complexity of the model.关键词
动物姿态估计/轻量型/高分辨率/注意力机制/空间与通道重构卷积Key words
animal pose estimation/lightweight/high-resolution/attention mechanism/spatial and channel reconstruc-tion convolution分类
信息技术与安全科学引用本文复制引用
宰清鹏,徐杨..融合空间与通道重构卷积和注意力的轻量型动物姿态估计[J].计算机工程与应用,2025,61(6):282-294,13.基金项目
贵州省科技计划项目(黔科合支撑[2023]一般326). （黔科合支撑[2023]一般326）
