Exoplanet imaging uses coronagraphs to block out the bright light from a star, allowing astronomers to observe the much fainter light from planets orbiting the star. However, these instruments are heavily impacted by small aberrations in the wavefront and require the minimization of starlight residuals directly in the focal plane. Stateof-the art wavefront control methods suffer from errors in the underlying physical models, and often require several iterations to minimize the intensity in the dark hole, limiting performance and reducing effective observation time. This study aims at developing a data-driven method to create a dark hole in post-coronagraphic images. For this purpose, we leverage the model-free capabilities of reinforcement learning to train an agent to learn a control strategy directly from phase diversity images acquired around the focal plane. Initial findings demonstrate successful aberration correction in non-coronagraphic simulations and promising results for dark hole creation in post-coronagraphic scenarios. These results highlight the potential of model-free reinforcement learning for dark-hole creation, justifying further investigation and eventually experimental validation on a dedicated testbed.
“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect small defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. The association of this examination technique with inspection in the visible spectrum, giving access to surface textures and geometries difficult to observe in the IR spectrum, can increase both robustness and performance of the defect detection. However in a deep learning approach, the acquisition of large amounts of visible-IR pairs can be difficult and time-consuming. The present work proposes to explore visible-FST image pairs generation in the context of surface crack detection for metallic materials, using state-of-the-art deep generative models such as Stable Diffusion. Both accuracy of the generated samples and benefits for multi-spectral deep neural models training will be studied.
“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyze and use contextual information from data and can be used for crack detection. However, in the literature only few works deal with the use of deep learning for the crack detection in FST. Indeed obtaining a large amount of data from FST examinations can be expensive and time-consuming. This work presents an open-access database for “flying spot” laser thermography, annotated for crack-type defect localization. It is followed by a benchmark of several state-of-the-art machine learning architectures. The benefits of this database for transfer to the detection and localization of cracks in coated metallic samples are highlighted, with very reduced amounts of data. Finally, this work gives a preliminary exploration on the use of individual thermal images, yielding to detection and localization performance comparable to the previously studied reconstructed thermal maps, which consists in the normalized mean of all the thermal images on a region of interest, after registration.
“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect small defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyse and use contextual information from data, and can be used for crack detection. However, in the literature only few works deal with the use of deep learning for the crack detection in FST. Indeed obtaining a large amount of data from FST examinations can be expensive and time-consuming. We propose here to build a generic, open-access dataset of laser thermography for defect detection. This database can be used by the community to develop new crack detection methods that can be benchmarked on the same database, as well as for pretraining networks for similar application tasks. We also present results of state of the art detection networks trained with the proposed database. These models give a basis for future works. Dataset, called FLYD (FLYing spot thermography Dataset), will be available in : https://github.com/kevinhelvig/FLYD/.
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