改进的RetinaFace复杂构件关键点检测算法OA北大核心CSTPCD

In order to improve the problems of difficulty in positioning of spraying operation,and lack of datasets in keypoints detection of complex aerospace components,models were built in the 3 D modeling software to make datasets of keypoints detection by taking screenshots and marking keypoints.Data augmentation methods were used in order to solve the problem of small sample size of datasets.On the basis of researching and improving existing RetinaFace keypoints detection algorithm,an optimized MobileNet structure was designed for the backbone feature extraction network and the learning rate was decayed by cosine warmup.The length of the input and output tensor was consistent with the number of keypoints corresponding to different components.The experimental results show that the average error on the validation set drops to 0.062 after 500 iterations of the model.The algorithm has better performance than similar algorithms,and can effectively identify the keypoints of components to be sprayed.