果实视觉:用于检测新鲜,腐烂和甲胺混合水果的基准数据集
Md Hasan Imam Bijoy1, Syeda Zarin Tasnim2, Syed Ali Awsaf2
1Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Data in brief
|July 3, 2025
概括
一个新的数据集对五种水果类型的水果质量进行了分类 (新鲜,腐烂,甲混合). 该资源有助于开发用于提高食品安全的自动化系统,并检测有害的甲混合产品.
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
- 数据科学数据科学数据科学
背景情况:
- 确保水果安全至关重要,特别是识别具有健康风险的甲胺混合样本.
- 甲是一种防腐剂,可以延长保质期,但对人类健康有害.
- 需要自动检测系统来有效和可靠地评估水果的质量.
研究的目的:
- 引入一个大规模的,验证的数据集,用于水果质量分类.
- 支持自动检测新鲜,腐烂和甲混合水果的研究.
- 促进食品安全监测系统的发展.
主要方法:
- 使用手机摄像头收集了10154张果,香,果,子和葡萄的图像.
- 应用了八种数据增强技术,将数据集扩展到81,232张图像.
- 为了模型兼容性,将所有图像重新定制为统一的512 × 512像素.
主要成果:
- 一个包含81,232张图像的综合数据集,涵盖五种水果类型和三种质量类别 (新鲜,腐烂,甲混合).
- 数据集在图像质量上表现出多样性,这是由于移动摄像机捕获的多样性.
- 统一的图像大小调整确保了机器学习应用程序的一致性.
结论:
- 该数据集是开发自动化水果分类和质量控制系统的宝贵资源.
- 它可以通过检测受损的产品来显著提高食品安全,从而有助于改善食品安全.
- 该数据集支持基于物联网的实时食品监测和甲素暴露风险减轻方面的进展.
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