To accomplish the task of aerial manipulator’s autonomous grasping， it is very important to recognize and locate the object. At present， most of the recognition algorithms of aerial manipulators use traditional feature extraction methods. In order to improve the accuracy and efficiency of object recognition and positioning， this paper designs a visual recognition and positioning algorithm based on YOLOv5 deep learning object detection algorithm and RGB-D sensor. The algorithm can detect the object in real-time and estimate its pose， which serves for the grasping task of aerial manipulator. At the same time， aiming at the problems that the deep learning algorithm has a huge amount of calculation and cannot achieve high-performance real-time detection in the embedded system， model quantization is introduced to optimize the algorithm， which can greatly improve the processing speed of the algorithm. This paper introduces the overall framework and implementation process of the algorithm and verifies the effectiveness of some algorithms of object detection and pose estimation by using the COCO dataset and motion capture system.