基于深度学习的多工件抓取点定位方法

Multiple Workpiece Grasping Point Localization Method Based on Deep Learning

针对工业生产线中多工件杂乱放置、互相遮挡,存在漏检、错检以及抓取点定位困难问题,提出一种基于协同深度学习的多工件抓取点定位方法。首先,以YOLOv5为基础网络,在输入端增加数据预处理模块用于图像增强时的角度变换,检测层增加特征细化网络,通过旋转锚框实现旋转工件的识别定位,采用轻量化的Ghost bottleneck模块代替主干网络中的bottleneckCSP模块,消除旋转锚框二次定位增加的时间成本,将融合后的特征图分别输入注意力机制模块,获取工件关键特征;其次,根据各工件检测框进行图像裁剪,将多工件检测近似转化为单工件检测;最后,求取工件质心,并结合旋转检测框的角度值确定抓取点。实验结果表明:所提方法有效解决了工件距离相近或互相遮挡时抓取点定位问题,且检测速度和精度均有明显优势,保证了工业场景中多工件检测的实时性。

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.