基于深度学习的人参饮片在线自动分选系统

IF 4.7 4区 医学 Q1 CHEMISTRY, MEDICINAL
Qilong Xue , Peiqi Miao , Kunhong Miao , Yang Yu , Zheng Li
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引用次数: 1

摘要

目的建立一种基于快速区域卷积神经网络(faster R-CNN)算法的深度学习架构,用于在线X射线机器视觉系统上对具有内部缺陷的红参进行自动检测和分类。方法用约2万个样本训练一个更快的基于R-CNN的分类器,平均精度值(mAP)为0.95。传统的基于前馈神经网络的图像处理方法性能较差,准确率、召回率和特异性分别为69.0%、68.0%和70.0%。因此,保存了Faster R-CNN模型来评估模型在有缺陷的红参在线分拣系统上的性能。结果使用一组独立的2000个红杜松子酒在三个平行测试中验证了基于Faster R-CNN的在线分拣系统的性能,准确率分别为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的无损检测中具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning

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 (Ginseng Radix et Rhizoma Rubra) 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.

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来源期刊
Chinese Herbal Medicines
Chinese Herbal Medicines CHEMISTRY, MEDICINAL-
CiteScore
4.40
自引率
5.30%
发文量
629
审稿时长
10 weeks
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