2023/06/04 by Yixiao Wang, Wang, Yixiao, Canlin Zhou +5
Computer Science · Engineering · #FOS: Physical sciences #Image Processing Techniques and Applications #Industrial Vision Systems and Defect Detection #Instrumentation and Detectors (physics.ins-det) #Optical measurement and interference techniques
paper · pdf · doi:10.48550/arxiv.2306.02234
openalex publication_date 2023/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Digital image correlation method is a non contact deformation measurement technique. Despite years of development, it is still difficult to solve the contradiction between calculation efficiency and seed point quantity.With the development of deep learning, the DIC algorithm based on deep learning provides a new solution for the problem of insufficient calculation efficiency in DIC.All supervised learning DIC methods requires a large set of high quality training set. However, obtaining such a dataset can be challenging and time consuming in generating ground truth. To fix the problem,we propose an unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image warp model is created to transform the target speckle image to the corresponding predicted reference speckle image by predicted 2D displacement map, the predicted reference speckle image is compared with the original reference speckle image to realize the unsupervised training of the CNN.The network's parameters are optimized using a composite loss function that incorporates both the Mean Squared Error and Pearson correlation coefficient.Our proposed method has a significant advantage of eliminating the need for extensive training data annotations. We conducted several experiments to demonstrate the validity and robustness of the proposed method. The experimental results demonstrate that our method can achieve can achieve accuracy comparable to previous supervised methods. The PyTorch code will be available at the following URL: https://github.com/fead1.