一种使用深度学习的用于缺陷 et 的在线自动分拣系统。 不过这里“et”不太明确具体所指，可能是有拼写错误或特定领域内未明确说明的术语。
An online automatic sorting system for defective et using deep learning.
作者信息
Xue Qilong, Miao Peiqi, Miao Kunhong, Yu Yang, Li Zheng
机构信息
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617 China.
State Key Laboratory of Component Traditional Chinese Medicine, Tianjin 301617, China.
出版信息
Chin Herb Med. 2023 Mar 15;15(3):447-456. doi: 10.1016/j.chmed.2023.01.001. eCollection 2023 Jul.
OBJECTIVE
To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng ( et ) with internal defects automatically on an online X-ray machine vision system.
METHODS
A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.
RESULTS
An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively.
CONCLUSION
The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
目的
基于更快的基于区域的卷积神经网络（Faster R-CNN）算法建立一种深度学习架构，用于在线X射线机器视觉系统上自动检测和分类有内部缺陷的红参。
方法
使用约20000个样本训练基于Faster R-CNN的分类器，平均精度均值（mAP）为0.95。基于前馈神经网络（FNN）的传统图像处理方法性能不佳，准确率、召回率和特异性分别为69.0%、68.0%和70.0%。因此，保存Faster R-CNN模型以评估其在有缺陷红参在线分拣系统上的性能。
结果
在三个平行测试中，使用一组独立的2000个红参来验证基于Faster R-CNN的在线分拣系统的性能，准确率分别达到95.8%、95.2%和96.2%。
结论
总体结果表明，所提出的基于Faster R-CNN的分类模型在红参内部缺陷无损检测方面具有巨大潜力。
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