Patent ID: 11906286
Assignee: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. According to claim 2, a deep learning-based temporal phase unwrapping method for fringe projection profilometry is characterized by step four wherein a residual convolutional neural network is built, consisting of six modules, including convolutional layers, pooling layers, concatenate layers, residual blocks, and upsampling blocks;
next, after the network is built, steps one to three are repeatedly performed to obtain multiple sets of data, which are divided into a training dataset, a validation dataset, and a test dataset; for the residual convolutional neural network, input data are set to be the wrapped phase maps with frequencies of 1 and 64, and output data are set to be the fringe order map of the high-frequency phase with frequencies of 64; to monitor the accuracy of the trained neural networks on data that they have never seen before, a validation dataset is created that is separate from training scenarios;
before training the residual convolutional neural network, the acquired data is preprocessed;because the fringe image obtained by the camera contains a background and the tested objects, and the background is removed by the following equation:, M
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          2, where M(x, y) is the intensity modulation in actual measurement; the modulation corresponding to points belonging to the background in the image is much smaller than the modulation corresponding to the points the tested objects, and the background in the image is removed by setting a threshold value; the data after the background removal operation is used as the dataset of the residual convolutional neural network for training; in the network configuration, a loss function is set as mean square error (MSE), a optimizer is Adam, and a training epoch is set as 500; the training dataset is used to train the residual convolutional neural network, the validation dataset is used to verify the performance of the trained network.