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猪肉新鲜度的智能分级方法基于几次的学习

Chao Liu^1,2, Jiayu Zhang¹, Kunjie Chen¹

¹College of Engineering, Nanjing Agricultural University, Nanjing 210000, China.

Foods (Basel, Switzerland)

|July 29, 2025

概括

本研究介绍了BBSNet,这是一种轻量级的几次学习模型,可以使用最小的图像准确地分类猪肉的新鲜度. 这种方法减少了对实时质量监测的数据依赖和成本.

科学领域:

人工智能的人工智能
食品科学食品科学食品科学
计算机视觉计算机视觉

背景情况:

猪肉新鲜度分级的深度学习需要大量的数据集,这阻碍了实际的,具有成本效益的应用.
高昂的数据收集成本限制了自动化猪肉质量评估的广泛采用.

研究的目的:

开发一个轻量级的几次拍摄学习模型 (BBSNet),以使用有限的图像数据准确地进行猪肉新鲜度分类.
为应对自动化食品质量监测系统数据短缺的挑战.

主要方法:

提出了BBSNet,这是一个新的架构,包含批量通道规范化 (BCN) 以提高特征的独特性.
利用BiFormer进行高效和细粒度的特征提取.
在600张猪肉图像的数据集上进行训练和评估,通过微生物细胞度评估新鲜度.

主要成果:

在一个具有挑战性的5向80射击学习任务中,达到96.36%的高平均准确率.
证明了模型在保持精度方面的有效性,尽管训练数据有限.
验证了BBSNet作为一个可行的解决方案,用于成本效益高,实时的猪肉质量监测.

结论:

BBSNet显著减少了对猪肉新鲜度分级的数据需求,为工业应用提供了实用的解决方案.
开发的框架将实验室新鲜度指标与在数据稀缺条件下的工业需求相结合.
未来的工作包括将BBSNet扩展到其他食品类型,并优化便携式设备的部署.

关键词:

双前 (BiFormer) 是一种双前.几次射击的学习学习精细调整精细调整猪肉的新鲜度猪肉的新鲜度

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