一种基于多域多尺度特征融合算法的高质量新鲜四川胡分类方法
Pengjun Xiang1,2, Fei Pan1,2, Xuliang Duan1,2
1College of Information Engineering, Sichuan Agricultural University, Ya'an 625014, China.
Foods (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一种新的多域YOLOv8模型,用于高效的四川胡收获后的选择. 先进的算法准确地细分和分类胡的质量,提高加工效率和生产商的利.
科学领域:
- 农业工程 农业工程
- 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:
- 收获后选择高质量的四川胡对于农业生产至关重要.
- 现有的方法在不同的胡姿势和成熟度水平上扎.
- 需要准确的视觉分析才能有效地进行分类.
研究的目的:
- 开发一个自动化的视觉系统,用于高质量的四川胡选择.
- 提高收获后加工的准确性和效率.
- 通过更好的质量控制,提高生产者的利能力.
主要方法:
- 拟议的多级频域特征融合模块 (MSF3M) 和多级双域特征融合模块 (MS-DFFM).
- 开发了一个多域YOLOv8网络,用于四川胡的细分和分类.
- 实施了基于平均本地像素值差异的选择方法.
主要成果:
- 多域YOLOv8-seg实现了新鲜四川胡细分的88.8%mAP50 (5.84 MB模型大小).
- 多域YOLOv8-cls在四川胡成熟度分类中达到98.34%的准确性.
- 多域YOLOv8模型表现出比基线YOLOv8.8更高的准确性和更轻的结构.
结论:
- 多域YOLOv8模型显著提高了四川胡的收获后加工效率.
- 开发的系统有效地减少了质量选择中的错误判断.
- 这项技术为农业应用和生产商利带来了巨大的好处.
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