Multiple Workpiece Grasping Point Localization Method Based on Deep Learning

In this study, a multi-workpiece grasping point location method based on collaborative depth learning is proposed to solve the problems of disorderly placement and mutual occlusion of multiple workpieces in industrial production lines, such as missing inspection, wrong inspection, and difficult grasping point location. First, YOLOv5 is used as the basic network, and a data preprocessing module is added at the input end for angle transformation during image enhancement. Subsequently, a feature thinning network is added to the detection layer to realize the recognition and positioning of rotating workpieces via rotating anchor frames, and a lightweight Ghost bottleneck module is used to replace the bottleneckCSP module in the backbone network to eliminate the increased time cost due to the secondary positioning of the rotating anchor frames. Additionally, the fused feature maps are inputted into the attention mechanism module to obtain the key features of the workpiece. Subsequently, the image is clipped based on each workpiece detection frame, and the multi-workpiece detection is approximately transformed into single workpiece detection. Finally, the center of mass of the workpiece is obtained, and the grasping point is determined by combining the rotation angles of the detection frame. The experimental results show that the proposed method effectively solves the problem of locating the grab points of multiple workpieces close to or occluding each other. Furthermore, the method has higher detection speed and accuracy, which guarantees the real-time performance of multi-workpiece detection in industrial scenes.