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Updated: Sep 15, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
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用深度学习算法对饼干质量进行分类
Oya Kilci1, Yusuf Eryesil2, Murat Koklu2
1Graduate School of Natural and Applied Sciences, Department of Mechatronics Engineering, Selcuk University, Konya, Türkiye.
Journal of food science
|July 18, 2025
概括
深度学习模型通过检测缺陷来显著改善饼干质量控制. EfficientNet实现了高精度,展示了自动化工业食品生产检查的潜力.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 食品生产中的传统质量控制是劳动密集型的,容易出现人为错误.
- 需要自动化缺陷检测系统来提高饼干制造的效率和降低成本.
研究的目的:
- 开发和评估用于自动检测有缺陷饼干的深度学习模型.
- 为了比较不同深度学习架构对二进制和多类缺陷分类的性能.
主要方法:
- 创建两个数据集:一个用于二进制分类 (缺陷/无缺陷) 和一个用于多类分类 (过度煮熟,质地缺陷,不完整).
- 深度学习模型的培训和评估,包括EfficientNet,ResNet,XceptionNet和MobileNet.
- 使用Grad-CAM用于模型可解释性,以可视化关注缺陷区域.
主要成果:
- 在二进制分类中,EfficientNet表现出卓越的性能,准确度为93.89%,在多类分类中为95.03%.
- ResNet取得了可比的结果,而XceptionNet和MobileNet显示了具有竞争力的F1分数,特别是在纹理缺陷方面.
- 格拉德-CAM分析证实了EfficientNet有效地专注于关键缺陷区域.
结论:
- 深度学习模型,特别是EfficientNet,为饼干生产中高效准确的自动化质量控制提供了可行的解决方案.
- 该研究强调了人工智能驱动的检查系统在工业食品制造中减少错误,时间和成本的潜力.
- 这些发现支持集成先进的机器学习技术,以加强食品产品质量保证.
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