基于改进YOLOv5的皮革抓取点识别及定位OACSTPCD

In order to achieve precise localization of leather grasping points by robots,this study proposed an improved approach based on the YOLOv5 algorithm.The methodology involved the integration of the coordinate attention mechanism into the Backbone layer and the replacement of the CIOU Loss with the Focal-EIOU Loss to enable different gradients and enhance the rapid and accurate recognition and localization of leather grasping points.The positioning coordinates of the leather grasping points were obtained by using the target bounding box regression formula,followed by the coordinate system conversion to obtain the three-dimensional coordinates of the target grasping points.The experimental positioning of leather grasping points was conducted by using the Intel RealSense D435i depth camera.Experimental results demonstrate the significant improvements over the Faster R-CNN algorithm and the original YOLOv5 algorithm.The improved YOLOv5 algorithm exhibited an accuracy enhancement of 6.9％and 2.63％,a recall improvement of 8.39％and 2.63％,and an mAP improvement of 8.13％and 0.21％in recognition experiments,respectively.Similarly,in the positioning experiments,the improved YOLOv5 algorithm demonstrated a decrease in average error values of 0.033m and 0.007m,and a decrease in error ratio average values of 2.233％and 0.476％.