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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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Related Experiment Video

Updated: Jul 19, 2025

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
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Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system.

Jiahao Wang1

  • 1College of Art and Design, Xi'an Mingde Institute of Technology, Xi'an, China.

Peerj. Computer Science
|August 7, 2023
PubMed
Summary

This study introduces G-Lite-DeepLabV3+, a novel image segmentation network for product packaging design. It enhances accuracy and efficiency in cyber-physical systems (CPS), improving packaging aesthetics and utility.

Keywords:
Attention mechanismCyber-physical systemsDeeplabv3Image segmentationProduct packaging design

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Packaging Design

Background:

  • Conventional image segmentation algorithms are time-consuming and may lose critical features, leading to suboptimal results in product packaging design.
  • Enhancing the aesthetic and practical aspects of packaging design requires accurate and efficient image segmentation.
  • Cyber-Physical Systems (CPS) offer a platform for integrating advanced image processing techniques.

Purpose of the Study:

  • To introduce a novel segmentation network, G-Lite-DeepLabV3+, for improved product packaging image segmentation within CPS.
  • To enhance the accuracy and efficiency of image segmentation for packaging design applications.
  • To address the limitations of conventional segmentation methods in capturing intricate details and processing speed.

Main Methods:

  • Developed G-Lite-DeepLabV3+ by replacing the feature extraction network of DeepLabV3 with Mobilenetv2.
  • Integrated group convolution and attention mechanisms to process complex semantic features and improve responsiveness.
  • Deployed the G-Lite-DeepLabV3+ network within a cyber-physical system (CPS) for remote, real-time image segmentation.

Main Results:

  • G-Lite-DeepLabV3+ demonstrated superior segmentation of diverse graphical elements in product packaging images.
  • Achieved a 3.1% increase in Intersection over Union (IoU) and a 6.2% improvement in mean Pixel Accuracy (mPA) compared to DeepLabV3+.
  • Significantly boosted processing speed by 22.1% (Frames Per Second - FPS), indicating enhanced efficiency.

Conclusions:

  • The G-Lite-DeepLabV3+ network effectively improves product packaging image segmentation accuracy and efficiency within CPS.
  • The integration of Mobilenetv2, group convolution, and attention mechanisms enhances feature processing and network performance.
  • This novel approach facilitates real-time, accurate segmentation, contributing to advanced packaging design and virtual environment applications.