保存蛋的内部质量的非破坏性检测和分类方法基于改进的ConvNext
Wenquan Tang1, Hao Zhang1, Haoran Chen1
1College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.
Foods (Basel, Switzerland)
|March 28, 2024
概括
本研究介绍了ConvNeXt_PEgg,这是一种用于自动化保存蛋质量评估的AI模型. 精致的ConvNeXt网络显著提高了分类和分级的准确性,提高了食品生产的效率.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 食品科学 食品科学 食品科学
背景情况:
- 保存蛋生产的低质量是一个持续的问题.
- 工人手工选择是低效的,主观的,昂贵的,阻碍了工业化.
研究的目的:
- 开发一种用于对保存蛋进行分类和分类的自动化系统.
- 为了解决手工质量评估在保存蛋生产中的局限性.
主要方法:
- 获得保存蛋的传输图像.
- 通过减少特征地图维度,整合多尺度特征融合 (MSFF),整合全球注意力机制 (GAM),并将交叉与焦点损失结合起来,完善了ConvNeXt网络.
- 开发了ConvNeXt_PEgg模型,用于保存蛋的分类和分级.
主要成果:
- ConvNeXt_PEgg模型在五个类别中实现了92.6%的分类准确率,并在三个级别中实现了95.9%的分级准确率.
- 与其前身相比,改进的模型显示参数体积减少了24.5%.
- 观察到分类和分级准确度的显著改善,优于经典模型.
结论:
- 精致的ConvNeXt_PEgg模型有效地对保存蛋进行分类和分类,为质量控制提供了可行的解决方案.
- 这种人工智能驱动的技术通过提高效率和准确性来提高保存蛋制造设施的经济可行性.
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