AI-Powered Device Mimics Human Vision for Precise, Low-Energy Color Recognition

Image Credit: Marc Schulte | Splash

Researchers at Tokyo University of Science have developed an AI-driven optoelectronic device that mimics human vision by distinguishing colors with high precision, according to a study published in Scientific Reports. The device, described as an artificial synapse, could enhance machine vision systems used in applications like autonomous vehicles.

Breakthrough in AI Vision Technology

The device, powered by dye-sensitized solar cells, replicates the human eye’s ability to differentiate colors by responding to red, green, and blue light with distinct electrical signals. Unlike traditional machine vision systems that require significant power and data storage, this self-powered device operates efficiently, making it suitable for edge-computing environments where resources are limited.

How It Works

The artificial synapse uses two dyes, D131 and SQ2, to produce positive or negative voltage outputs depending on the light’s wavelength. This allows it to distinguish colors with a resolution of 10 nanometers, achieving a six-bit resolution with 64 distinct states. The study highlights its ability to perform logic operations like AND, OR, and XOR within a single device, a key feature for AI processing.

Applications in Machine Vision

The device supports physical reservoir computing, a type of AI that processes time-series data. In tests, it classified color-coded human motions, such as jumping, with an accuracy of 82%. Researchers suggest this technology could improve optical sensors in autonomous vehicles, enabling them to interpret traffic lights, road signs, and other visual cues with minimal power consumption.

Implications for the Future

The study, led by Hiroaki Komatsu, Norika Hosoda, and Takashi Ikuno, marks a step toward energy-efficient AI systems that emulate human visual processing. By reducing reliance on external power and complex circuits, the device could lead to more compact and sustainable machine vision solutions.

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Source: Brighter Side of News, Nature

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