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相关实验视频

Updated: May 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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深度学习模型压缩和硬件加速,用于在家禽肉上高性能异物检测,使用NIR超光谱成像.

Zirak Khan1, Seung-Chul Yoon2, Suchendra M Bhandarkar1

  • 1School of Computing, University of Georgia, Athens, GA 30602, USA.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
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通过硬件加速和定量化优化用于高光谱成像 (HSI) 的深度学习推断,可以显著加快在家禽加工中检测异物. 这使得高线路速度的实时质量控制成为可能,确保更安全的食品.

科学领域:

  • 食品科学与技术 食品科学与技术
  • 计算机视觉 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 食品安全依赖于在家禽中检测异物.
  • 超光谱成像 (HSI) 为污染物检测提供了丰富的数据.
  • 深度学习 (DL) 模型提供高精度,但面临实时处理挑战.

研究的目的:

  • 优化深度学习推断,用于在家禽加工中基于高光谱成像的异物检测.
  • 为了应对高维度,计算复杂性和实时实施的挑战.

主要方法:

  • 应用训练后量化 (FP16) 以减少模型大小和计算负载.
  • 使用了NVIDIA TensorRT的硬件加速,以提高推理速度.
  • 模拟的高光谱线扫描摄像头用于工业条件评估.

主要成果:

  • 获得的推断时间与家禽加工线速度 (140-250只/分钟) 兼容.
  • 与传统的GPU推断相比,推断时间减少了多达五倍.
  • 在保持高检测准确度的同时,模型尺寸减少了50%.

结论:

关键词:
深度学习是一种深度学习.外来物质检测检测外来物质检测硬件加速加速器 硬件加速器超光谱成像技术的使用.工业应用 工业应用推理优化推理优化培训后的量化定量化在家禽加工加工方面,实时处理实时处理.传感器技术传感器技术传感器技术

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  • 集成后训练量化和硬件加速是有效的实时DL推断HSI数据.
  • 优化的模型显示了在工业家禽加工中实际部署的潜力.
  • 这种方法克服了计算瓶,提高了食品质量和安全.