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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point served as...

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语境增强网络与空间意识图形用于智能手机屏幕缺陷检测

Aili Liang1, Qishan Wang2, Xiaofeng Wu1

  • 1School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了CE-SGNet,这是一种用于检测智能手机屏幕玻璃缺陷的新型深度学习模型. 它有效地识别了小的,低对比度的缺陷,提高了屏幕质量.

关键词:
注意力机制注意力机制检测缺陷检测检测缺陷检测的方法图形推理 图形推理 图形推理智能手机屏幕是智能手机的屏幕.

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科学领域:

  • 计算机视觉 计算机视觉
  • 材料科学 材料科学 材料科学
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 触摸屏设备无处不在,增加了对高质量的屏幕玻璃的需求.
  • 在屏幕玻璃中检测缺陷对于智能手机制造质量至关重要.
  • 现有的深度学习方法面临着小,不规则,低对比度缺陷的挑战.

研究的目的:

  • 为准确的智能手机屏幕缺陷检测提出一个先进的深度学习模型.
  • 为了解决检测小尺寸,不规则形状和低对比度的缺陷的局限性.

主要方法:

  • 开发了CE-SGNet,结合了自适应感应场注意模块 (ARFAM) 和空间感知图形推理模块 (SGRM).
  • 在ARFAM中,可自适应地提取上下文信息以增强缺陷特征.
  • SGRM使用图形注意网络来编码缺陷区域之间的空间关系.

主要成果:

  • CE-SGNet在识别和定位各种屏幕玻璃缺陷方面表现出色.
  • 该模型准确地检测到公共数据集上的各种形状和尺寸的缺陷.
  • 增强的特征表示提高了检测准确性和稳定性.

结论:

  • CE-SGNet有效地克服了在检测微妙的屏幕玻璃缺陷方面的挑战.
  • 拟议的网络显著提升了智能手机屏幕质量检查的最新技术.
  • 这种方法有望改善自动化视觉检查系统.