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A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
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Owl-vision-inspired near sensor computing.

Zishen Zhao1,2, Yixin Cao1,3, Shuaiwei Huang1

  • 1School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.

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|February 12, 2026
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Summary
This summary is machine-generated.

Inspired by owl vision, this study introduces a novel bimodal synaptic transistor for low-light machine vision. The device enables efficient, high-adaptation passive target detection in photon-starved conditions.

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Passive target detection is vital for machine vision in low-light conditions, enabling applications like surveillance and guidance.
  • Existing technologies struggle with sensitivity and adaptation in photon-starved environments.
  • Mimicking biological vision systems, like that of owls, offers a promising approach for enhanced performance.

Purpose of the Study:

  • To develop a bimodal synaptic transistor inspired by owl vision.
  • To achieve optoelectronic decoupling for parallel photonic perception and electrical plasticity emulation.
  • To enable energy-efficient, low-light image processing for neuromorphic vision sensors.

Main Methods:

  • Fabrication of a bimodal synaptic transistor with an optoelectronic decoupling mechanism.
  • Characterization of the device's performance in perceiving low light intensities.
  • Demonstration of synaptic weight modulation for artificial neural network applications.
  • Testing adaptive contrast enhancement for image processing.

Main Results:

  • The device achieved a high active adaptation index of approximately 331.
  • It successfully detected light intensities as low as 0.146 nW cm-2.
  • Cyclically stable synaptic weight modulation (enhancement and inhibition) was demonstrated.
  • Feasible weight deployment across three artificial neural levels was verified over a specific light intensity range.

Conclusions:

  • The developed owl-vision-inspired device provides a hardware foundation for advanced neuromorphic vision sensors.
  • It enables efficient and effective low-light image processing.
  • This technology has significant potential for applications requiring high sensitivity and adaptation in challenging visual environments.