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一个基于高光谱成像的水果自主监督异常探测器

Yisen Liu¹, Songbin Zhou¹, Zhiyong Wan¹

¹Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China.

Foods (Basel, Switzerland)

|July 29, 2023

概括

这项研究引入了一种新的自我监督异常检测方法,用于使用高光谱成像进行水果质量控制. 频谱空间异常检测 (SSAD) 方法有效地识别了水果缺陷,而没有先前的缺陷样本.

科学领域:

农业科学农业科学
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:

超光谱成像 (HSI) 和化学测量对于水果质量评估至关重要.
无监督的异常检测对于识别水果缺陷至关重要,因为准备缺陷样本的难度很大.

研究的目的:

为水果缺陷检测提出一个光谱空间,基于信息的,自我监督的异常检测 (SSAD) 框架.
开发一种有效的方法,用于在高光谱水果数据中无监督检测异常.

主要方法:

一种自我监督的学习方法,使用辅助分类器来识别主要组件图像投影轴.
使用已学习的分类器的完全连接层作为光谱空间特征提取器.
采用特征相似度指标用于异常评估.

主要成果:

在草和蓝数据集中,SSAD方法实现了卓越的异常检测性能,平均AUC为0.923.
可视化证实了SSAD在提取相关的光谱空间隐藏表示中的有效性.
在训练数据中存在异常样本时,SSAD证明了稳定性,并且表现优于基线方法.

结论:

拟议的SSAD框架提供了一种强大的无监督方法,用于使用高光谱成像检测水果缺陷.
SSAD为水果行业的质量控制提供了有效的解决方案,克服了缺陷样本准备方面的挑战.
该方法的稳定性使其适用于现实世界的应用,其中培训数据可能不完美.

关键词:

检测缺陷检测检测缺陷检测的方法果实质量控制果实质量控制接近红外的高光谱成像技术自主监督学习学习

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