基于锚点的快速三维手部关键点检测算法OA

In human-robotcollaboration tasks,hand key point detection provides target point coordinates for the robotic arm.A2J(Anchor-to-Joint)is a representative method of key point detection using anchor points.A2J can achieve better detection effect with depth map input,but it has insufficient ability to acquire global features.In this study,a GLF(Global-Local Feature Fusion)module is designed to fuse the shallow and deep features of the backbone network.In order to improve the detection speed,the backbone network of A2J is replaced with Shuffle-Netv2 and reformed,and 3×3 depth separable convolution is replaced with 5×5 depth separable convolution to in-crease the sensitivity field and effectively improve the backbone network's ability to extract global features.ECA(Effi-cient Channel Attention)is introduced into the anchor weight estimation branch to improve the network's attention to important anchor points.The results of training and testing on the mainstream data sets ICVL and NYU show that the average error of the proposed method is reduced by 0.09 mm and 0.15 mm,respectively,compared with A2J.The detection rate of 151 frame·s-1 is realized on GTX1080Ti graphics card,which fully meets the real-time require-ment of man-machine collaboration task.