Abstract
［Objective］ To achieve fine classification of eggs and improve the accuracy of egg appearance detection.［Methods］ An egg appearance detection scheme based on improved convolutional neural network (CNN ) and hierarchical support vector machine (SVM ) was proposed.① Egg images with different orientations and appearances were captured using an egg machine vision image acquisition device,and image enhancement techniques were applied to expand the egg image database.② An improved Coati optimization algorithm (COA ) and fuzzy C -means (FCM ) clustering algorithm were designed,based on which the structure and hyperparameters of the CNN model were optimized to enhance its generalization ability.The optimized CNN was then used for deep learning on the egg image database to effectively extract features from egg appearance images.③ A hierarchical SVM was established for fine classification of egg appearance,ultimately achieving accurate detection and classification of egg appearance.［Results］ The detection accuracy of the proposed egg appearance detection scheme improved by 1.74%~4.31%,and the detection time was reduced by 21.68%~53.51%.［Conclusion］ The proposed method effectively enables online real -time fine classification of eggs.
Publication Date
4-3-2025
First Page
158
Last Page
164
DOI
10.13652/j.spjx.1003.5788.2024.60138
Recommended Citation
Wanpeng, YAO; Lingxiao, ZHANG; Xiaofeng, ZHAO; and Feicheng, WANG
(2025)
"Egg appearance detection based on improved CNN and hierarchical SVM,"
Food and Machinery: Vol. 41:
Iss.
1, Article 20.
DOI: 10.13652/j.spjx.1003.5788.2024.60138
Available at:
https://www.ifoodmm.cn/journal/vol41/iss1/20
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