The Workpiece Sorting Method Based on Improved YOLOv5 For Vision Robotic Arm

Abstract

In order to solve the problem of high error rate and poor real-time performance in the workpieces sorting process for traditional industrial robotic arms, this paper designed a vision robotic arm testing platform with real-time processing ability, and proposes a kind of workpieces sorting method based on improved YOLOv5 used to the vision robotic arm. By replacing the focus layer in the YOLOv5 backbone network, embedding the coordinate attention module, which re-weights the feature maps from the channel and spatial, improves the object detection accuracy of the YOLOv5 model. The workpiece sorting test platform consists of an NVIDIA Jetson nano controller and a vision robotic arm. The hand-eye calibration of the robotic arm is completed by the Zhang Zhengyou calibration method and the TsarLenz method. The workpiece target image was collected, tagged and data augmented to create the target workpiece dataset. And use TensorRT to optimize the inference acceleration of the model to adapt to the hardware platform requirements. The test shows that the improved YOLOv5 model can well ensure the stable operation of the test platform, and improve the accuracy and real -time performance of workpiece target recognition.