Avian eye-inspired perovskite artificial vision system for foveated and multispectral imaging.


Journal

Science robotics
ISSN: 2470-9476
Titre abrégé: Sci Robot
Pays: United States
ID NLM: 101733136

Informations de publication

Date de publication:
29 May 2024
Historique:
medline: 29 5 2024
pubmed: 29 5 2024
entrez: 29 5 2024
Statut: ppublish

Résumé

Avian eyes have deep central foveae as a result of extensive evolution. Deep foveae efficiently refract incident light, creating a magnified image of the target object and making it easier to track object motion. These features are essential for detecting and tracking remote objects in dynamic environments. Furthermore, avian eyes respond to a wide spectrum of light, including visible and ultraviolet light, allowing them to efficiently distinguish the target object from complex backgrounds. Despite notable advances in artificial vision systems that mimic animal vision, the exceptional object detection and targeting capabilities of avian eyes via foveated and multispectral imaging remain underexplored. Here, we present an artificial vision system that capitalizes on these aspects of avian vision. We introduce an artificial fovea and vertically stacked perovskite photodetector arrays whose designs were optimized by theoretical simulations for the demonstration of foveated and multispectral imaging. The artificial vision system successfully identifies colored and mixed-color objects and detects remote objects through foveated imaging. The potential for use in uncrewed aerial vehicles that need to detect, track, and recognize distant targets in dynamic environments is also discussed. Our avian eye-inspired perovskite artificial vision system marks a notable advance in bioinspired artificial visions.

Identifiants

pubmed: 38809996
doi: 10.1126/scirobotics.adk6903
doi:

Substances chimiques

perovskite 12194-71-7
Calcium Compounds 0
Oxides 0
Titanium D1JT611TNE

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eadk6903

Auteurs

Jinhong Park (J)

Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea.
School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.

Min Seok Kim (MS)

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.

Joonsoo Kim (J)

Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea.
School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.

Sehui Chang (S)

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.

Mincheol Lee (M)

Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea.
School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
Electro-Medical Equipment Research Division, Korea Electrotechnology Research Institute (KERI), Ansan 15588, Republic of Korea.

Gil Ju Lee (GJ)

Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea.

Young Min Song (YM)

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
Artificial Intelligence (AI) Graduate School, GIST, Gwangju 61005, Republic of Korea.

Dae-Hyeong Kim (DH)

Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea.
School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea.

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