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Updated: Jun 1, 2025

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基于多光谱成像的果伤的检测,使用细分网络和分类模型

Yanru Fang¹, Hongyi Bai^1,2, Laijun Sun^1,2

¹College of Electronics and Engineering, Heilongjiang University, Harbin, China.

Journal of food science

|January 20, 2025

概括

这项研究引入了一种使用多光谱成像的深度学习方法,以准确检测果伤水平和时间. 该方法显著改善了伤检测和分类,为水果行业提供了一个新的工具.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

伤会对果的外观,营养价值和营销能力产生负面影响,导致经济损失.
准确及时检测伤对于果行业的质量控制至关重要.

研究的目的:

开发和验证一种用于精确检测果伤水平和时间的新方法.
通过深度学习和多光谱成像来提高伤细分和分类的准确性.

主要方法:

一个自行设计的多谱成像系统与一个改进的DeepLabV3+模型相结合,用于伤细分.
使用深度可分离的卷积,高效的通道注意力和焦点损失来提高细分精度.
来自伤区域的光谱数据使用改进的DenseNet121进行了分析,用于伤水平和时间识别,并结合了共弦和注意力机制.

主要成果:

改进的DeepLabV3+在伤细分方面实现了高交叉超过联合 (IoU) 得分 (高达95.5%) 和F1得分 (高达97.5%).
增强的DenseNet121模型在识别伤水平 (精度高达99.5%) 和伤时间 (精度高达99.3%) 中表现出卓越的性能.

结论:

提出的基于深度学习的多谱成像方法为检测果伤水平和时间提供了高度准确和有效的解决方案.
这项技术有可能显著减少经济损失,并改善果生产和供应链的质量控制.

关键词:

伤水平和果的时间.伤的地区提取提取改进了DeepLabV3+的使用情况.改进了DenseNet121的使用情况.多光谱成像技术多光谱成像技术

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