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一个具有机器视觉系统的深度学习模型,用于在食物消费过程中识别食物的类型

Pouya Bohlol¹, Soleiman Hosseinpour², Mahmoud Soltani Firouz¹

¹Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering, University of Tehran, Karaj, Iran.

Scientific reports

|August 13, 2025

概括

这项研究使用机器视觉和深度学习来准确识别消费的食品,支持可持续发展目标. 带有狮子优化器的EfficientNetB7模型在16个食品类中实现了100%的准确性,在32个食品类中达到99%.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
食品科学食品科学食品科学

背景情况:

食品行业需要强有力的质量控制和产品知识,重点关注数量,新鲜度和颜色.
可持续发展目标 (SDGs) 强调控制食品消费,促进健康,减少能源使用,并尽量减少对环境的影响.

研究的目的:

开发和评估用于识别消费食品的机器视觉和深度学习系统.
通过自动化食品识别来解决与食品消费,健康,能源和环境影响相关的可持续发展目标.

主要方法:

创建了一个食品消费图像和视频的数据集,分为16个和32个类别,并进行增强.
评估了9个深度学习架构,确定EfficientNetB5,B6和B7是最有效的.
进行了超参数调整,并将Adam和Lion优化器与EfficientNetB7模型进行了比较.

主要成果:

带有狮子优化器的EfficientNetB7模型在16个食品类别中实现了100%的准确性.
对于32种食品类别,该模型的准确度达到99%,误差指标较低 (MAE:0.0079,MSE:0.035,RMSE:0.18).
优化的深度学习架构在调整后的数据集中显示出强度.

结论:

机器视觉和深度学习,特别是带有Lion优化器的EfficientNetB7模型,为识别消费的食品提供了高度准确的方法.
这项技术支持加强食品质量控制,并有助于实现相关的可持续发展目标.

关键词:

深度学习是一种深度学习.有效网B7 有效网B7 食品分类食品分类食品分类食物消费食物消费食物消费食品的认可食品的认可机器视觉机器视觉机器视觉

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