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一个基于深度学习的传感系统,用于识别鱼和彩虹鱼肉,并为消费者保护分类新鲜度.

Hong-Dar Lin1, Jun-Liang Chen1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

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概括

这项研究引入了一个基于智能手机的系统,使用深度学习来打击海鲜欺诈和评估鱼的新鲜度. 它使消费者能够实时识别鱼类和分类质量,提高食品安全.

关键词:
密集的网络121深度学习是一种深度学习.鱼肉的分类 鱼肉的分类食品欺诈行为 食品欺诈行为新鲜度分级 新鲜度分级转移学习转移学习

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科学领域:

  • 食品科学 食品科学 食品科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 海鲜欺诈,就像错误标记鱼类一样,引发了严重的食品安全问题,并侵犯了消费者权利.
  • 准确识别和评估海鲜的新鲜度对于消费者信任和安全至关重要.

研究的目的:

  • 开发基于深度学习的智能手机兼容系统,用于鱼肉识别和鱼新鲜度等级.
  • 为消费者提供基于图像的实时工具,用于验证海鲜的真实性和质量.

主要方法:

  • 采用了两阶段的深度学习方法,首先对鱼类进行分类,然后对鱼的新鲜度进行分级.
  • 使用了改进的DenseNet121架构,具有全球平均聚合,丢失和自定义输出层.
  • 用部分层结进行转移学习,以优化训练效率.

主要成果:

  • 与一阶段方法和基线模型相比,两阶段方法在分类和分级方面表现优越.
  • 该系统在识别鱼类和评估鱼新鲜度水平方面取得了强大的准确性.
  • 灵敏度分析显示了对图像扭曲的弹性,例如模糊和相机倾斜.

结论:

  • 开发的系统为海鲜认证和新鲜度评估提供了一个实用,以消费者为中心的解决方案.
  • 这项技术有可能显著提高食品安全,并保护海鲜市场的消费者权利.
  • 需要进一步的研究来应对现实世界的挑战,例如可变的照明和包装条件.