Surface defects (SDs) and subsurface defects (SSDs) are the key factors decreasing the laser damage threshold of optics. Due to the spatially stacked structure, accurately detecting and distinguishing them has become a major challenge. Herein a detection method for SDs and SSDs with multisensor image fusion is proposed. The optics is illuminated by a laser under dark field condition, and the defects are excited to generate scattering and fluorescence lights, which are received by two image sensors in a wide-field microscope. With the modified algorithms of image registration and feature-level fusion, different types of defects are identified and extracted from the scattering and fluorescence images. Experiments show that two imaging modes can be realized simultaneously by multisensor image fusion, and HF etching verifies that SDs and SSDs of polished optics can be accurately distinguished. This method provides a more targeted reference for the evaluation and control of the defects of optics, and exhibits potential in the application of material surface research.

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Surface defects (SDs) and subsurface defects (SSDs) are the key factors decreasing the laser damage threshold of optics. Due to the spatially stacked structure, accurately detecting and distinguishing them has become a major challenge. Herein a detection method for SDs and SSDs with multisensor image fusion is proposed. The optics is illuminated by a laser under dark field condition, and the defects are excited to generate scattering and fluorescence lights, which are received by two image sensors in a wide-field microscope. With the modified algorithms of image registration and feature-level fusion, different types of defects are identified and extracted from the scattering and fluorescence images. Experiments show that two imaging modes can be realized simultaneously by multisensor image fusion, and HF etching verifies that SDs and SSDs of polished optics can be accurately distinguished. This method provides a more targeted reference for the evaluation and control of the defects of optics, and exhibits potential in the application of material surface research.

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.