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Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback.

Haoting Liu¹, Qianxiang Zhou², Jin Yang³

¹School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. imkyran@hotmail.com.

Sensors (Basel, Switzerland)

|February 18, 2017

Summary

This study introduces an intelligent desk lighting system using an imaging sensor for precise environmental light perception. The system automatically tunes Light Emitting Diode (LED) lamps for optimal lighting effects based on real-time luminance changes.

Keywords:

environment perception imaging sensor intelligent lighting luminance control vision effect

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Area of Science:

Optoelectronics and Lighting Systems
Computer Vision and Image Processing
Human-Computer Interaction

Background:

Traditional intelligent lighting systems often lack precise environmental light perception.
Photosensitive resistance and infrared sensors have limitations in accurately assessing ambient light conditions.
Advanced sensing is required for sophisticated lighting control in desk applications.

Purpose of the Study:

To propose an imaging sensor-based intelligent Light Emitting Diode (LED) lighting system for enhanced desk illumination.
To develop a system capable of finer perception of environmental light for more precise lighting control.
To achieve optimal luminance tuning through automated LED lamp adjustment.

Main Methods:

Accumulation of typical imaging lighting data for desk applications.
Definition and assessment of subjective and objective Lighting Effect Evaluation Metrics (LEEMs).
Development of single and multiple LEEMs-based control strategies for luminance tuning.
Real-time image capture via a wearable camera and LEEMs computation.
Comparison of computed LEEMs with established cluster benchmarks for control implementation.

Main Results:

The proposed system demonstrates the ability to automatically tune LED lamps in response to environmental luminance fluctuations.
Objective LEEMs were defined and used to establish cluster benchmarks for lighting quality.
Both single and multiple LEEMs-based control methods were successfully developed and tested.
Experimental results validate the system's effectiveness in achieving optimal lighting conditions.

Conclusions:

An imaging sensor-based intelligent LED lighting system offers superior environmental light perception compared to traditional methods.
The developed LEEMs and control strategies enable precise and automated adjustment of desk lighting.
The system effectively optimizes luminance for improved user experience and lighting quality.