Abstract

A lens defect is a common quality issue that has seriously harmed the scattering characteristics and performance of optical elements, reducing the quality consistency of the finished products. Furthermore, the energy hotspots coming from the high-energy laser through diffraction of optical component defects are amplified step by step in multi-level laser conduction, causing serious damage to the optical system. Traditional manual detection mainly relies on experienced workers under a special light source environment with high labor intensity, low efficiency, and accuracy. The common machine vision techniques are incapable of detecting low contrast and complex morphological defects. To address these challenges, a deep learning-based method, named STMask R-CNN, is proposed to detect defects on the surface and inside of a lens in complex environments. A Swin Transformer, which focuses on improving the modeling and representation capability of the features in order to improve the detection performance, is incorporated into the Mask R-CNN in this case. A challenge dataset containing more than 3800 images (18000 defect sample targets) with five different types of optical lens defects was created to verify the proposed approach. According to our experiments, the presented STMask R-CNN reached a precision value of 98.2%, recall value of 97.7%, F1 score of 97.9%, mAP@0.5 value of 98.1%, and FPS value of 24 f/s, which outperformed the SSD, Faster R-CNN, and YOLOv5. The experimental results demonstrated that the proposed STMask R-CNN outperformed other popular methods for multiscale targets, low contrast target detection and nesting, stacking, and intersecting defects sample detection, exhibiting good generalizability and robustness, as well as detection speed to meet mechanical equipment production efficiency requirements. In general, this research offers a favorable deep learning-based method for real-time automatic detection of optical lens defects.

© 2023 Optica Publishing Group

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