US 11,906,286 B2
Deep learning-based temporal phase unwrapping method for fringe projection profilometry
Qian Chen, Nanjing (CN); Chao Zuo, Nanjing (CN); Shijie Feng, Nanjing (CN); Yuzhen Zhang, Nanjing (CN); and Guohua Gu, Nanjing (CN)
Assigned to NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY, Nanjing (CN)
Appl. No. 17/280,464
Filed by Nanjing University of Science and Technology, Jiangsu (CN)
PCT Filed Jul. 5, 2019, PCT No. PCT/CN2019/094884
§ 371(c)(1), (2) Date Mar. 26, 2021,
PCT Pub. No. WO2020/063013, PCT Pub. Date Apr. 2, 2020.
Claims priority of application No. 201811149287.9 (CN), filed on Sep. 29, 2018.
Prior Publication US 2021/0356258 A1, Nov. 18, 2021
Int. Cl. G01B 11/25 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06N 3/049 (2023.01)
CPC G01B 11/25 (2013.01) [G06N 3/049 (2013.01); G06N 3/08 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A deep learning-based temporal phase unwrapping method for fringe projection profilometry is characterized in that the specific steps are as follows:
step one, four sets of three-step phase-shifting fringe patterns with different frequencies (including 1, 8, 32, and 64) are projected to tested objects; the projected fringe patterns are captured by a camera simultaneously to acquire four sets of three-step phase-shifting fringe images;
step two, the three-step phase-shifting fringe images acquired by the camera are processed to obtain a wrapped phase map using a three-step phase-shifting algorithm;
step three, a multi-frequency temporal phase unwrapping (MF-TPU) algorithm is used to unwrap four wrapped phase maps successively to obtain a fringe order map and an absolute phase map of a high-frequency phase with 64 periods;
step four, a residual convolutional neural network is built to implement phase unwrapping; 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; 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;
step five, the residual convolutional neural network after training and validation makes predictions on the test dataset to realize a precision evaluation of the network and output the fringe order map of the high-frequency phase with 64 periods.