Abstract:In the field of aeronautics and astronautics, the assembly accuracy of precision devices such as inertial gyroscope is required to be high. At present, most of them are assembled manually, which has low assembly efficiency and the assembly process is easily influenced by human subjective. In view of the above problems, this paper adopts the objection detection algorithm based on Faster R-CNN, extracts the feature information through vgg16 network, and uses the depth network model of COCO data set for migration training in the process of model training to prevent the model from over-fitting and accelerating the training process of parameters. At the same time, the method is compared with other deep learning models and traditional algorithms, and is tested on the self built data model test set. The results show that the Fast R-CNN object detection model based on vgg16 has obvious advantages for the detection of inertial gyro in the complex environment and when the object is blocked, the accuracy can reach 87.80%, the recall rate 80.30%, and the recognition speed can reach 15fps, which can meet the real-time requirements.