结合混合注意力与多尺度特征的人体姿态估计OA

To solve the problem of low accuracy of multi-person pose estimation in occlusion scenes,a human pose estimation model named DAW-YOLOPose,which combines mixed attention mechanism and multi-scale sequence feature is proposed.Firstly,the mixed local channel attention(MLCA)mech-anism is used to improve the backbone network of YOLOv8Pose,effectively capturing and transmitting spatial and channel information without increasing the number of model parameters,so as to improve the feature expression effect of the network.Secondly,a new multi-scale sequence feature fusion net-work is proposed to enhance the extraction ability of multi-scale feature information and integrate feature maps of different scales.Finally,the gradient gain allocation strategy of Wise-IoU v3 loss function is used to improve the ability to distinguish high-quality anchor frames and reduce the negative impact of low-quality samples on model training.The experimental results on MSCOCO dataset show that,com-pared with YOLOv8Pose,DAW-YOLOPose improves the mAP@0.5,mAP@0.5:0.95 and recall by 2.7 percentage points,1.4 percentage points and 1.9 percentage points respectively,achieving a better estimation effect.