基于SGV-YOLOv8模型的机械零件智能识别与抓取方法OA

To solve the problems of slow part identification and low success rate in grabbing mechani-cal parts by industrial robots,an intelligent part identification and grabbing method was proposed based on SGV-YOLOv8 model.The monocular camera and laser ranging module were used to build a depth vision detection device to realize the three-dimensional positioning of mechanical parts;Taking the YOLOv8 model as the basic architecture,StarNet network was used in the backbone network to replace the original structure,and GSConv module and VoV-GSCSP structure were introduced in the neck,so as to reduce the complexity of the model and improve the detection speed and capture rate.The experimental results show that compared with the original model,the number of model parameters and the number of floating point operations(GFLOPs)of the designed SGV-YOLOv8 increases 51.9%and 51%respectively,while the number of detection frames per second(FPS)increases 37.6%;The success rate of part grasp-ing in the constructed industrial robot grasping devices is 80%.