目的 解决传统机器视觉机器人抓取系统对多目标及复杂目标背景分割不精确导致的目标定位精度差而影响机器人抓取效率的问题，提出一种新的深度学习抓取识别定位系统。方法 搭建由Delta机械臂、PC上位机、双目相机等组成的硬件系统，对工业部署常用的YOLO系列算法进行对比研究。将YOLO与U-NET相结合，用于目标的检测和分割。在精确分割出属于目标和背景目标的像素区域的同时，计算边缘和中心位置信息，运用立体视觉技术得到三维位置，并转换为世界坐标系，由PC机引导机械臂去完成抓取任务。结果 深度学习目标检测和图像分割相结合的系统在较复杂背景、多目标的场景下比未添加图像分割的算法拥有更好的目标定位精确度。结论 YOLOv5和U-NET相结合的目标定位抓取方法具有较高的鲁棒性，达到了并联机械臂的抓取要求。该方法能够运用于其他多自由度机械臂上，具有良好的应用价值。

The work aims to propose a new deep learning grasping recognition and positioning system, in order to solve the problem of poor target positioning accuracy caused by the inaccuracy of multi-target and complex-target background segmentation in traditional machine vision robot grasping system. A hardware system composed of Delta robot arm, PC host computer and binocular camera was built to compare and study the YOLO series algorithms commonly used in industrial deployment. YOLO and U-NET were combined for target detection and segmentation. When the pixel regions belonging to the target and the background target were divided, the edge and center position information were calculated, the three-dimensional position was obtained by stereo vision technology and converted into the world coordinate system, and the robot arm was guided by the PC to complete the grasp. The system combining deep learning target detection and image segmentation had better target positioning accuracy than the algorithm without image segmentation in complex background and multi-target scenes. The target positioning and grasping method combining YOLOv5 and U-NET has high robustness and meets the grasping requirements of parallel robot arms. This method can be applied to other multi-degree-of-freedom robot arms and has good application value.