Deep Learning-Based Interference Fringes Detection Using Convolutional Neural Network

Abstract

The interference fringes of interferometry are the key to reconstruct a three-dimensional topography. But currently the adjustment of the fringes is done by manual, which is time-consuming and lack of quantitative control. Due to the complexity of the fringes, the traditional methods have low recognition rates and are only suitable for the ideal fringes in specific cases. Therefore, an interference fringes discovery (IFD) model consisting of 'fringes region proposal network' (FRPN) and 'fringes stitching' (FS) model is proposed. The FRPN, a deep convolutional neural network modified on Faster R-CNN, accurately recognizes the fringes with identification boxes. By integrating the feature maps of multiple layers and fine-Tuning, the ability of the modified network to extract more complicated fringes is improved. Based on the identification boxes generated by FRPN, the FS restores the fringe shape and generates a complete recognition area. The IFD model achieves 98% accuracy on our testing set. The experimental results confirm that our model has the excellent performance on fringes-detection. It provides important support for the automation and the precise measurement of interferometers.