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An automatic sorting system for unwashed eggs using deep learning
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DOI：10.1016/j.jfoodeng.2020.110036.png)
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摘要
En 中文
Egg quality and safety are significant concerns of consumers and modern food industries. This study proposes a novel and precise assessment of egg sorting using a deep convolutional neural network (CNN), which is a state-of-the-art computer vision method to perform classification tasks. To classify unwashed egg images, VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. The modified model was trained based on intact, bloody, and broken (breakage, crack, or hole on the eggshell) eggs, which were combined with being dirty. Performance evaluation of the CNN model through 5-fold cross-validation showed that it outperforms traditional machine vision-based models. The accuracy, precision, sensitivity, specificity, and area under the curve were 96.55, 95.59, 94.92, 97.39, and 96.16%, respectively. The CNN model achieved an average overall accuracy of 94.84 by 5-fold cross-validation.
Keyword:
Egg
Intact
Defect detection
Deep learning
VGG16
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期刊
IF:
5.8
论文数:
1.1W
被引数:
3.3W
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