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在客户操作的鱼类加工系统中,使用机器视觉的AI驱动分类和精密切割算法.

Hossein Azarmdel1,2,3, Seyed Saeid Mohtasebi4, Ali Jafary5

  • 1Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. h.azarmdel@ut.ac.ir.

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|November 25, 2025
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概括

本研究介绍了一种使用人工智能 (AI) 的智能鱼类加工系统,用于分类鱼类并确定精确的切割点. 这项创新旨在通过自动化加工来克服感官障碍并促进鱼类消费.

关键词:
背光照明的细分照明切割点 切割点 切割点鱼类的分类 鱼类的分类机器视觉 机器视觉 机器视觉支持矢量机器 (SVM) 是一个支持矢量机器.

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科学领域:

  • * 农业工程 农业工程
  • * 人工智能 * 人工智能
  • * 食品科学 食品科学

背景情况:

  • * 鱼类消费受到臭味和繁重加工的限制.
  • *现有的鱼类加工系统需要人工干预来切割和清洁.
  • * 感官障碍,如鱼的气味,减少消费者的吸引力.

研究的目的:

  • * 通过整合人工智能 (AI) 来开发智能鱼类加工系统.
  • *使用人工智能算法对高消费鱼类进行分类.
  • * 创建自动化切割点确定算法,用于增强鱼类加工.

主要方法:

  • *使用人工智能对四种高消费鱼类进行分类.
  • * 开发使用背光蓝色背景的切割点确定算法.
  • *基于平均平方误差 (MSE) 和准确度的人工神经网络 (ANN) 和支持矢量机 (SVM) 分类器的评估.

主要成果:

  • * ANN模型实现了高精度 (99.62%的列车,95.06%的测试) 与一个最佳的6-23-4结构.
  • * SVM分类器表现出强的性能,99.69%的列车和98.75%的测试准确度.
  • *对于银鱼 (98.36%,99.49%),鱼 (97.85%,98.07%) 和鱼 (96.61%,97.90%) 实现了精确的头部和腹部切割点.

结论:

  • *人工智能驱动的分类和自动切割点的确定大大提高了鱼类加工.
  • *智能系统有效地解决了与鱼味和手工加工相关的挑战.
  • *开发的算法显示出高精度,为更高效的鱼类加工系统铺平了道路.