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利用预先训练的计算机视觉模型来准确分类肉的新鲜度

Marcelo M Hidalgo¹, Robson C Lima¹, Elisabete A De Nadai Fernandes¹

¹Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil.

|October 4, 2025

概括

这项研究引入了一种使用深度学习和随机编码的新方法,可以从图像中准确评估肉类的新鲜度. 这种方法提供了一种更快,非破坏性的方法来确保食品质量和安全.

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:

越来越多的消费者对安全和高质量的食品的需求需要可靠的方法来评估肉的新鲜度.
传统的肉类新鲜度评估方法往往耗时,破坏性或主观.
深度学习为快速,非破坏性地分析食物特性提供了潜力.

研究的目的:

开发和评估一种新的,高效的,非破坏性的方法,通过图像分析来分类肉类的新鲜度.
为了利用预先训练的深度卷积神经网络 (DCNN) 和随机编码的聚合深度激活地图 (RADAM) 来进行特征提取.
为了评估在这些提取的特征上训练的机器学习 (ML) 分类器的性能.

主要方法:

使用预先训练的DCNN来从肉类图像中提取深度特征.
应用了RADAM来编码这些深层特征.
训练有素的传统ML分类器使用RADAM编码的特性.
验证了三种不同的牛肉和肉新鲜度数据集的方法.

主要成果:

实现了最先进的分类性能,准确度指标从93%到100%不等.
与文献中报道的现有方法相比,证明了更高的性能.
该方法被证明比目前的方法更简单,更有效.

结论:

采用DCNN和RADAM的基于图像的方法是肉类新鲜度分类的高效和准确方法.
这种技术为食品行业的现实应用提供了实用和高效的解决方案.
这些发现支持了广泛行业部署的潜力,以加强食品质量和安全监测.

关键词:

计算机视觉计算机视觉计算机视觉肉类质量评估肉类质量评估模式识别模式识别模式识别质感分析质感分析

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