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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Design of library intelligent lighting system based on deep learning.

Hong Wang1,2, Yingjie Sheng1,2, Yang Guo3,4

  • 1College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.

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|August 31, 2025
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Summary
This summary is machine-generated.

This study introduces an intelligent lighting system for university libraries using an improved YOLOv5 algorithm for accurate human detection and localization. This smart system enhances energy efficiency and library intelligence.

Keywords:
Deep learningIndoor localizationMultilayer perceptronSimulation experimentTarget detectionYOLOv5

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Smart Systems Engineering

Background:

  • Current university library lighting systems suffer from low intelligence and high energy consumption.
  • There is a need for automated and adaptive lighting solutions to improve user experience and reduce energy waste.

Purpose of the Study:

  • To design and implement an intelligent lighting solution for university libraries based on sub-area autonomy control.
  • To enhance human target detection and spatial localization accuracy for adaptive lighting.

Main Methods:

  • An improved YOLOv5 algorithm incorporating a coordinate attention mechanism and depth-separable convolution for human target detection.
  • A multilayer perceptron (MLP) model for mapping 2D human coordinates to 3D spatial localization.
  • Optimization of the bounding box regression loss function in YOLOv5.

Main Results:

  • Achieved a mean average precision (mAP) of 91.4% for human body detection.
  • Demonstrated fast and accurate localization of multiple people within the library space.
  • Experimental results showed a 6.9% point increase in mean average accuracy compared to baseline methods.

Conclusions:

  • The proposed intelligent lighting system effectively performs human body detection and localization.
  • The system offers a practical and referenceable solution for developing intelligent lighting in academic libraries.
  • This approach significantly improves the intelligence and energy efficiency of library lighting systems.