猪色:猪肉颜色分类的深度学习框架
Yuxian Pang1, Chuchu Chen1, Yuedong Yang1
1Sun Yat-sen University, No. 132 Waihuandong Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Meat science
|December 18, 2024
概括
这项研究引入了一种新的深度学习模型,用于客观评估猪肉颜色,改善食品安全和质量评估. 该模型使用标准化数据和先进的图像处理技术实现了高精度.
科学领域:
- 食品科学 食品科学 食品科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 猪肉的颜色对于评估安全性和新鲜度至关重要,但人类的评估是主观和低效的.
- 现有的计算机视觉和深度学习方法缺乏标准化数据和强大的预处理,限制了性能.
- 图像中的背景噪声会对自动化猪肉颜色分析的准确性产生负面影响.
研究的目的:
- 开发用于猪肉图像收集的标准化方法和数据集.
- 提出一种新的深度学习模型,用于准确预测猪肉颜色.
- 提高猪肉质量评估的客观性和可靠性.
主要方法:
- 设计了一种标准化的猪肉图像收集设备,并策划了1707张图像的数据集.
- 开发了一个由两个模块组成的深度学习框架:图像预处理和颜色分类.
- 利用分段任何模型 (SAM) 来消除背景噪音,并使用ResNet-101进行基于补丁的训练进行分类.
主要成果:
- 在定制数据集上实现了91.50%的分类准确性.
- 在外部验证数据集上获得了89.00%的准确性.
- 通过有效的图像预处理,证明了改进的模型稳定性和准确性.
结论:
- 拟议的深度学习模型为猪肉颜色分析提供了可靠和客观的方法.
- 标准化数据收集和先进的预处理显著提高模型性能.
- 波尔科罗尔在线应用程序为现实世界的应用提供了一个实用的工具.
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