智慧农业（中英文）2025，Vol.7Issue(3)：160-172,13.DOI:10.12133/j.smartag.SA202502001
基于改进HRNet的高精度鱼类姿态估计方法
High-Precision Fish Pose Estimation Method Based on Improved HRNet
摘要
Abstract
[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or deficient,they of-ten display abnormal behaviors and noticeable changes in the positioning of their body parts.Moreover,the unpredictable posture and orientation of fish during swimming,combined with the rapid swimming speed of fish,restrict the current scope of research in FPE.In this research,a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points.[Methods]On the one hand,this model incorporated the CBAM module into the HRNet framework.The attention module enhanced accuracy without adding computational complexity,while effec-tively capturing a broader range of contextual information.On the other hand,the model incorporated dilated convolution to increase the receptive field,allowing it to capture more spatial context.[Results and Discussions]Experiments showed that compared with the baseline method,the average precision(AP)of HPFPE based on different backbones and input siz-es on the oplegnathus punctatus datasets had increased by 0.62,1.35,1.76,and 1.28 percent point,respectively,while the average recall(AR)had also increased by 0.85,1.50,1.40,and 1.00,respectively.Additionally,HPFPE outperformed oth-er mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other meth-ods using the ornamental fish data,HPFPE achieved the highest AP and AR values of 52.96%,and 59.50%,respectively.[Conclusions]The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns,serving as a valuable reference for applications such as fish behavior recognition.关键词
水产养殖/计算机视觉/鱼类姿态估计/关键点/注意力机制Key words
aquaculture/computer vision/fish pose estimation/key point/attention mechanism分类
农业科技引用本文复制引用
彭秋珺,李蔚然,刘业强,李振波..基于改进HRNet的高精度鱼类姿态估计方法[J].智慧农业（中英文）,2025,7(3):160-172,13.基金项目
National Key Research and Development Program of China(2020YFD0900204) （2020YFD0900204）
National Science and Technology Major Project(2021ZD0113805) （2021ZD0113805）
Beijing Smart Agriculture Innovation Consortium Project(BAIC10-2024) 国家重点研发计划项目(2020YFD0900204) （BAIC10-2024）
新一代人工智能国家科技重大专项(2021ZD0113805) （2021ZD0113805）
北京市智慧农业创新团队项目(BAIC10-2024) （BAIC10-2024）
