MeatScan:一种基于机器学习的图像数据集,用于对新鲜和腐烂的牛肉进行分类
Rose-Mary Owusuaa Mensah Gyening1, Michael Appiah Akoto1, Kwabena Owusu-Agyemang1
1Kwame Nkrumah University of Science and Technology, Department of Computer Science, Ghana.
Data in brief
|October 27, 2025
概括
MeatScan是一种新的图像数据集,用于使用深度学习将牛肉分类为新鲜或腐烂. 这一数据集有助于在现实世界,低资源环境中进行食品安全检查.
科学领域:
- 计算机视觉 计算机视觉
- 食品科学 食品科学 食品科学
- 机器学习 机器学习
背景情况:
- 计算机视觉中的深度学习进展尚未广泛应用于实际的食品安全检查,特别是在资源有限的地区.
- 对肉类新鲜度进行准确的分类对于公共卫生和预防食源性疾病至关重要.
研究的目的:
- 介绍MeatScan,一种新的高分辨率RGB图像数据集,用于二进制分类牛肉 (新鲜或腐烂).
- 支持深度学习模型的开发和评估,用于在不同的现实环境中自动化监测食品质量.
主要方法:
- 策划了来自加纳市场和储存设施的11000张高分辨率RGB牛肉图像.
- 图像被训练有素的收藏家在自然光下根据视觉线索 (颜色,纹理,表面状况) 标记为新鲜或受损.
- 为监督学习设计的数据集,容纳卷积神经网络,转移学习和数据增强.
主要成果:
- 肉Scan包含5627个新鲜和5373个腐烂的肉类图像,反映了现实世界的情况.
- 数据集的各种图像捕捉环境 (市场,商店,仓储) 引入了照明和背景的变化.
- 它作为一个基准来评估模型的稳定性与肉质和环境因素的变化.
结论:
- 肉扫描解决了在食品安全检查中对结构化视觉数据的关键需求,特别是在资源较少的环境中.
- 该数据集有助于研究肉类质量评估的强大深度学习模型.
- 它可以在具有挑战性的现实条件下评估模型性能,弥合AI和食品安全实践之间的差距.
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