%0 Journal Article %A GUAN Liwen %A SUN Xinlei %A YANG Pei %T Grasping Detection Based on Key Point Estimation %D 2022 %R 10.3778/j.issn.1002-8331.2008-0436 %J Computer Engineering and Applications %P 267-274 %V 58 %N 4 %X Grasping is an important capability of human-machine coordination for robots in both service and industrial scenes. Obtaining an accurate grasp detection result is the key for the manipulator to complete the grasp task. In order to improve the accuracy and real-time performance of grasp detection, a grasp detection algorithm based on key point estimation which is improved from CenterNet is proposed. Firstly, in the feature extraction layer of the network, the feature fusion method is used to fuse different feature graphs for reducing feature loss. Secondly, the angle prediction branch is added to predict the grasp angle. Finally, improved Focal Loss is used to reduce the reduction of accuracy caused by the imbalance of positive and negative samples. Different from the anchor-based grasp detection algorithm, which enumerates the potential location of the object and then the object is measured by regression method. The key point estimation method directly predicts the grasp key points, then based on the key points, predicts the size, offset and angle of the target. The experimental results show that compared with the anchor-based grasp detection, the proposed method is more efficient, accurate and simple. The model achieves 97.6% accuracy and 42 frame/s detection speed on the Cornell grasp dataset. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2008-0436