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相关实验视频

Updated: Jun 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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基于深度神经网络的香烟过器缺陷检测系统与FPGA加速用于在线识别.

Liang Huang1, Qiongxia Shen2, Chao Jiang2

  • 1School of Electronic Information and Communications, Huazhong University of Science & Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括

这项研究引入了一种用于检测香烟缺陷的AI模型,达到95.88%的准确性. 该系统使用RESNET18和FPGA部署来实现制造业中的高速实时质量控制.

关键词:
深度神经网络是一个神经网络.发现缺陷检测检测缺陷检测现场可编程的门阵列.实时实时的时间.

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科学领域:

  • 制造业 制造技术 制造技术
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 机器视觉和人工智能在香烟制造中用于缺陷检测.
  • 目前的方法难以高精度和实时检测复杂的香烟图案.

研究的目的:

  • 为香烟开发一个准确和高速的缺陷检测模型.
  • 解决现有的实时缺陷检测系统的局限性.

主要方法:

  • 提出了一个基于RESNET18的模型与功能增强算法相结合.
  • 该模型部署在现场可编程网关阵列 (FPGA) 上,用于并行处理.

主要成果:

  • 拟议的模型在香烟过器缺陷数据集上实现了95.88%的检测准确度.
  • 实现了9.38毫秒的端到端检测速度.

结论:

  • 开发的模型有效地提高了对香烟缺陷的检测准确度.
  • 通过FPGA的部署,可以在香烟制造中高速实时检测缺陷.