Grasp detection based on deep learning is an important method for robots to accurately perceive unstructured environments. However, the deep learning method widely used in general object detection is not suitable for robotic grasp detection. Multi-stage network is often designed to meet the requirements of grasp posture, but they increase computation complexity. This paper proposes a single-stage robotic grasp detection method by using region proposal networks. The proposed method generates multiple oriented reference anchors firstly. The grasp rectangles are then regressed and classified based on these anchors. A new matching strategy for oriented anchors is proposed based on the rotation angles and center positions of the anchors. The well-known Cornell grasp dataset and Jacquard dataset are used to test the performance of the proposed method. Experimental results show that the proposed method can achieve higher grasp detection accuracy compared with other methods in the literature.

基于深度学习的抓取检测是机器人准确感知非结构化环境的重要方法。但是，广泛用于一般物体检测的深度学习方法不适用于机器人抓握检测。通常设计多级网络来满足抓握姿势的要求，但它们会增加计算复杂性。提出了一种利用区域提议网络的单阶段机器人抓握检测方法。该方法首先生成了多个定向参考锚。然后根据这些锚点对抓取矩形进行回归和分类。根据锚的旋转角度和中心位置，提出了一种新的定向锚定匹配策略。使用著名的康奈尔抓地数据集和提花数据集来测试该方法的性能。