Design of ultra-wide FOV LWIR staring imaging system

Yan, Song; Zhang, Jianjun; Wang, Rixing

doi:10.1007/s12596-023-01094-0

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The importance of 3D thermal mapping cannot be overstated. LWIR sensors often face a trade-off between resolution and field of view (FOV) limited to objective factors such as fewer infrared band materials, difficulty in aberration correction, and the limited number of optical elements, the design of ultra-wide FOV infrared staring imaging system [3]. By generating 3D thermal maps, it becomes possible to predict heat conduction and effectively expand the FOV, thereby creating value.…”

Section: Introductionmentioning

confidence: 99%

Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation

Kim,

Kim

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Machine Learning has played a major role in various applications including Visual Slam and themal image process. In this paper, we discussed the possibility of generating a thermal map using LWIR images and a deep learning-based visual slam network and the value that the thermal map can create. We summarized the advantages and applicability of various deep learning-based visual slams and confirmed the results of nice slam, which generates the most curious Dense map. In order to apply Visual SLAM technology, time series, scene repetition, and images from various angles for one scene are required. However, most LWIR data sets consist of one shot for each scene or are unidirectional driving data. To solve this, we created a scenario using the LWIR driving dataset and created a repetitive route through repetition. RGB-Depth SLAM Mapping was performed on the constructed data set, and the results were evaluated and the limitations of the current approach were discussed. Finally, we summarized future directions for creating stable 3D thermal maps in indoor and outdoor environments by resolving the limitations.

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Section: Introductionmentioning

confidence: 99%

Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation

Kim,

Kim

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Design strategy for an automotive lens with a wide field of view

Xie,

Sun,

Xue

et al. 2024

Appl. Opt.

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To solve the problems in designing a wide field of view (FOV) automotive lens, such as the complicated initial structure solution when using aberration theory and the limited scaling method, a novel design strategy, to the best of our knowledge, based on the compression ratio of field angle is proposed. The wide-FOV automotive lens is first divided into two groups, front and rear lenses, and each group is designed with several parallel plates as starting points using SYNOPSYS software. Then, the two groups are connected as a whole initial optical system. Finally, an iterative process is applied to improve the performance of the initial structure. A wide-FOV automotive lens is designed to verify the feasibility and effectiveness of the design method proposed in this paper. The wide-FOV automotive lens has the characteristics of good imaging quality, large aperture, and small size and consists of six spherical lenses. The result of the design indicates that this method based on the compression ratio of field angle is simple, effective, fast in convergence, and provides a good starting point for the design of a wide-FOV automotive lens.

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Design of ultra-wide FOV LWIR staring imaging system

Cited by 2 publications

References 9 publications

Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation

Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation

Design strategy for an automotive lens with a wide field of view

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