使用超光谱成像与化学测量和目标检测算法相结合,检测的异物
Zhan Shu1, Xiong Li1, Yande Liu1
1School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Foods (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种新的方法,用于使用高光谱成像和物体检测算法检测中的异物. 综合方法实现了高精度和速度,确保食品安全和产品质量.
科学领域:
- 食品科学与技术 食品科学与技术
- 频谱学是一种光谱学.
- 计算机视觉 计算机视觉
背景情况:
- 在加工过程中容易受到视觉上类似的外来物质的污染.
- 手动检查和传统的分拣方法不足以检测这些污染物,对消费者健康构成风险.
- 确保产品的食品安全需要先进的检测技术.
研究的目的:
- 开发和验证一个系统,以增强检测中视觉相似的外来物质.
- 为了提高异物识别和定位的准确性和速度.
- 通过先进技术,确保产品的安全性和质量.
主要方法:
- 超光谱成像用于从样品中捕获详细的光谱信息.
- 用光谱模式识别算法 (随机森林,SVM,最小距离) 来进行图像增强和像素分类.
- 对象检测算法 (R-CNN,更快的R-CNN,YoloV5) 用于在增强图像中识别和定位异物.
主要成果:
- 随机森林算法在图像增强过程中实现了像素样本识别精度高达86%.
- YoloV5的异物识别率超过96%,检测时间约为12 ms.
- 综合系统成功检测到各种异物,包括红石,塑料,织物和纸张.
结论:
- 超光谱成像,光谱图案识别和物体检测的结合提供了一个强大的解决方案,用于检测中具有挑战性的异物.
- 这项技术为食品行业的实时批量检测系统提供了宝贵的理论框架.
- 该方法有可能用于识别其他颗粒食品中的异物.
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