A Multi-Object Grasping Detection Based on the Improvement of YOLOv3 Algorithm

Abstract

YOLOv3 has achieved good results in the field of object detection. In order to achieve multi-object grasping detection, the network structure has been improved. The improved YOLOv3 algorithm is applied to the object position and pose detection in robotic grasping, and a deep learning model is proposed to predict the robot's grasping position, which can detect the occurrence of multiple objects in real time and grasp them in order according to the semantic information. For the specific application scenario, the corresponding dataset is made, and a corner detection method based on YOLOv3 is proposed to grasping position and pose detection. Compared with the traditional corner detection method, this method has semantic information in its detected corner. In the scene, we first classify and locate the object, then detect the corner of the object, and filter the corner of the false detection through the positioning of the object, and design the corresponding algorithm to complete the corner of the missed detection, so that the accuracy of the corner detection is greatly improved, reaching 99% in the self-made dataset. Finally, the position information of the corner is used to calculate the centroid position of the object, that is, the grasping point of the object. The point cloud information is obtained by depth camera, and the grasping pose of the object is calculated. This method can greatly improve the accuracy of grasping detection in specific scenes.