Binocular vision mechanical arm system based on salient region target recognition

Abstract

Robotic arms have been widely used in the sorting of manufacturing lines. During the sorting process of the robot arm, the captured speed and accuracy are core problems. In order to improve that, this paper proposes a binocular vision sorting robot arm system based on the target identification of significant regions. First, establish the target artifact training set and the Faster R-CNN algorithm is imported. A binocular camera is used to take pictures of the workpiece in the tray, and then deep learning algorithm is used to extract the significant area where the object is located. Image processing is performed on this region. Then, the three-dimensional coordinates of the workpiece centroid are calculated and passed to the manipulator for grasping and sorting. Because the significance area is extracted, the amount of calculation by the image processing algorithm is greatly reduced, and the speed is significantly improved. Finally, the picking and sorting experiment was designed. The results show that the system has high accuracy, strong robustness and strong portability.