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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

Updated: Jan 8, 2026

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior
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实时零售计划程序合规应用程序使用计算机视觉和虚拟货架.

Tsung-Yin Ou1,2, Andrés Ponce3, Cody Lee3

  • 1Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC. outy@nkust.edu.tw.

Scientific reports
|December 16, 2025
PubMed
概括

这项研究引入了便利店的自动货架监控系统,使用计算机视觉和深度学习来确保规划程序的合规性. 该系统实现了高精度和效率,优于智能零售的手动审计.

关键词:
自动标签的自动化标签集群化过程是指集群化过程.计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.符合计划程序的合规性虚拟货架是虚拟的货架.

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科学领域:

  • 在零售分析中使用计算机视觉和深度学习.
  • 自动货架监控系统 自动货架监控系统
  • 智能零售技术 智能零售技术

背景情况:

  • 在便利店中手动的计划图审计是低效的,昂贵的,容易出现错误.
  • 需要自动化,可靠的解决方案来监测货架合规性.
  • 扩展性和准确性是实施自动零售监控的关键挑战.

研究的目的:

  • 为便利店开发和部署一个可扩展的,自动化的货架监控系统.
  • 通过计算机视觉和深度学习来改善计划程序的合规性.
  • 为传统的手动审计提供一个具有成本效益和准确的替代方案.

主要方法:

  • 整合计算机视觉和深度学习,用于货架和产品检测.
  • 开发一个定制的对齐算法,用于将货架布局与数字平面图进行比较.
  • 实施多图像拼接,以创建虚拟架子并提高适应性.
  • 创建大型数据集,用于模型培训和验证,并自动化标签流程.

主要成果:

  • 基于YOLOv8的模型实现了高精度和回收 (99.23%精度,98.93%回收) 和产品检测 (94.61%精度,93.02%回收).
  • 基于ResNet101和FAN的变压器模型表现出强大的稳定性,在真实数据上准确率为99.86%.
  • 基于FAN的模型在少数射击实验中表现出极好的适应性,在未见的产品上达到98.39%的Top-1精度.

结论:

  • 拟议的自动化系统提供了高精度,可扩展性和实时效率,以实现规划程序的合规性.
  • 这项技术可以作为手动审计的可行替代方案,推动智能零售领域的创新.
  • 该系统的适应性和性能突显了AI在优化零售业务方面的潜力.