[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 def...[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 often 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 effectively 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 sizes 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 other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods 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.展开更多
为解决复杂跨域场景下猪个体的目标检测与计数准确率低下的问题,该研究提出了面向复杂跨域场景的基于改进YOLOv5(You Only Look Once version 5)的猪个体检测与计数模型。在骨干网络中分别集成了CBAM(Convolutional Block Attention Mod...为解决复杂跨域场景下猪个体的目标检测与计数准确率低下的问题,该研究提出了面向复杂跨域场景的基于改进YOLOv5(You Only Look Once version 5)的猪个体检测与计数模型。在骨干网络中分别集成了CBAM(Convolutional Block Attention Module)即融合通道和空间注意力的模块和Transformer自注意力模块,并将CIoU(Complete Intersection over Union)Loss替换为EIoU(Efficient Intersection over Union)Loss,以及引入了SAM(Sharpness-Aware Minimization)优化器并引入了多尺度训练、伪标签半监督学习和测试集增强的训练策略。试验结果表明,这些改进使模型能够更好地关注图像中的重要区域,突破传统卷积只能提取卷积核内相邻信息的能力,增强了模型的特征提取能力,并提升了模型的定位准确性以及模型对不同目标大小和不同猪舍环境的适应性,因此提升了模型在跨域场景下的表现。经过改进后的模型的m AP@0.5值从87.67%提升到98.76%,m AP@0.5:0.95值从58.35%提升到68.70%,均方误差从13.26降低到1.44。该研究的改进方法可以大幅度改善现有模型在复杂跨域场景下的目标检测效果,提高了目标检测和计数的准确率,从而为大规模生猪养殖业生产效率的提高和生产成本的降低提供技术支持。展开更多
文摘[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 often 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 effectively 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 sizes 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 other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods 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.
文摘为解决复杂跨域场景下猪个体的目标检测与计数准确率低下的问题,该研究提出了面向复杂跨域场景的基于改进YOLOv5(You Only Look Once version 5)的猪个体检测与计数模型。在骨干网络中分别集成了CBAM(Convolutional Block Attention Module)即融合通道和空间注意力的模块和Transformer自注意力模块,并将CIoU(Complete Intersection over Union)Loss替换为EIoU(Efficient Intersection over Union)Loss,以及引入了SAM(Sharpness-Aware Minimization)优化器并引入了多尺度训练、伪标签半监督学习和测试集增强的训练策略。试验结果表明,这些改进使模型能够更好地关注图像中的重要区域,突破传统卷积只能提取卷积核内相邻信息的能力,增强了模型的特征提取能力,并提升了模型的定位准确性以及模型对不同目标大小和不同猪舍环境的适应性,因此提升了模型在跨域场景下的表现。经过改进后的模型的m AP@0.5值从87.67%提升到98.76%,m AP@0.5:0.95值从58.35%提升到68.70%,均方误差从13.26降低到1.44。该研究的改进方法可以大幅度改善现有模型在复杂跨域场景下的目标检测效果,提高了目标检测和计数的准确率,从而为大规模生猪养殖业生产效率的提高和生产成本的降低提供技术支持。
