Semi-supervised anchorless single-engine grip detection

Abstract

The extensive application of artificial intelligence in wireless communication, 3D reconstruction, and target location has successfully solved the modeling problem. Grasping specific objects in a stacked scene is a difficult task to achieve robot grasping. In this paper, a semi-supervised anchor-less single-gun grasping detection framework is proposed to help robots grasp objects easily. The framework first designs the AFMaskDetDep (AFMDD) network and then predicts the contours of objects by looking at sensing modules. Finally, the boundary value of the minimum boundary rectangle is obtained by judging the optimal target and the optimal grasping point of the inference module, and the final result is obtained by rotating back to the coordinate output on the original image area. Our proposed approach yields state-of-the-art performance with improved accuracy and computational speed on the VMRD and Cornell datasets, respectively. Experiments show that the algorithm can help the robot grasp a single specific object and has a high success rate in overlapping scenes. The success rate of each grip exceeds 93.5%.