Abstract

The tendency to increase the accuracy and quality of optical parts inspection can be observed all over the world. The imperfection of manufacturing techniques can cause different defects on the optical component surface, making surface defects inspection a crucial part of the manufacturing of optical components. Currently, the inspection of lenses, filters, mirrors, and other optical components is performed by human inspectors. However, human-based inspections are time-consuming, subjective, and incompatible with a highly efficient high-quality digital workflow. Moreover, they cannot meet the complex criteria of ISO 10110-7 for the quality pass and fail optical element samples. To meet the high demand for high-quality products, intelligent visual inspection systems are being used in many manufacturing processes. Automated surface imperfection detection based on machine learning has become a fascinating and promising area of research, with a great direct impact on different visual inspection applications. In this paper, an optical inspection platform combining parallel deep learning–based image-processing approaches with a high-resolution optomechanical module was developed to detect surface defects on optical plane components. The system involves the mechanical modules, the illumination and imaging modules, and the machine vision algorithm. Dark-field images were acquired using a ${{2448}} \times {{2048}}$-pixel line-scanning CMOS camera with 3.45 µm per-pixel resolution. Convolutional neural networks and semantic segmentation were used for a machine vision algorithm to detect and classify defects on captured images of optical bandpass filters. The experimental results on different bandpass filter samples have shown the best performance compared to traditional methods by reaching an impressive detection speed of 0.07 s per image and an overall detection pixel accuracy of 0.923.

© 2021 Optical Society of America

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