期刊
APPLIED OPTICS
卷 61, 期 15, 页码 4344-4353出版社
Optica Publishing Group
DOI: 10.1364/AO.453851
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资金
- State Key Laboratory of Modern Optical Instrumentation [MOI2021ZD01]
- Fundamental Research Funds for the Central Universities [2021XZZX019]
- National Key Research and Development Program of China [2021YFC2202001]
The presence of bubbles in optics can affect their performance, making the detection of bubbles an important step for quality assurance. Current manual inspection methods lack precision and consistency. To improve the quality evaluation process, a real-time detection method based on deep learning is proposed, enabling accurate and timely detection of the position and size of bubbles in optics.
The existence of bulk bubbles could decrease the laser-induced damage threshold of optics and affect the beam quality, so the detection of bulk bubbles is an essential step for quality assurance. Currently, the inspection of bubbles in optics relies on manual work, which is not recommended because of the low precision and inconsistency. To improve the quality evaluation process, a real-time detection method for bubbles inside the optics based on deep learning is proposed. Our method can implement bubble detection at 67 fps with a recall of 0.836. As for retrieval of the radius, it costs 58.8 ms on each bubble, and the absolute deviation is 3.73% on average. Our method conducts real-time and accurate detection of the positions and radii of the bubbles in the optics, thus, having significant potential for the manufacturing process. (C) 2022 Optica Publishing Group
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