| Abstrakt: |
Aiming at the problems that the current robot grasping detection method is too discrete in predicting the grasping angle and the grasping process may produce large off-angle, which reduces the grasping detection accuracy and even leads to grasping failure, an improved robot real- time grasping detection method based on the YOLOv5 neural network model is proposed. Firstly, the grasping frame coordinates and grasping angles are extracted based on the single-stage object detection model YOLOv5. Afterwards, the grasping angles are divided more carefully, while circular smoothing label is introduced to accommodate the periodicity of the angles, links between adjacent angles are established, the YOLOv5 detection head is decoupled, and the loss function is optimized to improve the detection accuracy. Finally, an experimental validation is performed on the Cornell dataset. The experimental results show that the proposed algorithm can better predict the grasping angle and improve the grasping detection accuracy compared with the classical grasping detection methods. The model achieves 97.5% accuracy and 71 FPS detection speed on the Cornell dataset. [ABSTRACT FROM AUTHOR] |
