A Two-stage Grasp Detection Algorithm Based on Improved YOLOv5
A two-stage grasp detection algorithm is proposed for the disorderly grasping needs of robots in complex scenes.The network model of YOLOv5 is improved by attention fusion of shallow location information and deep semantic information on multi-scale feature fusion to improve the detection of multi-scale targets.The rejection factor is introduced into the loss function to enhance the robustness of the model in occlusion environment.The grasp target bounding box is cropped after the target detection to avoid the interference from the rest of the targets during the grasp detection process.The grasp detection algorithm is improved by introducing the CSP structure and attention mechanism to improve the feature extraction ability of the model.In grasping multi-target obscured objects randomly placed in a real environment,the results show that the robot has a 95%success rate.
万方数据
维普
