通过使用深度学习,自动化蛋损伤检测,以改善食品行业的质量控制.
Talha Alperen Cengel1, Bunyamin Gencturk2, Elham Tahsin Yasin2
1Department of Computer Engineering, Technology Faculty, Selcuk University, Konya, Turkey.
Journal of food science
|January 22, 2025
概括
深度学习模型可以准确地检测受损的蛋. 在识别裂和表面缺陷方面,GoogleLeNet取得了最高的准确性 (98.73%),改善了蛋行业的质量控制.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 食品科学 食品科学 食品科学
背景情况:
- 对于食品行业来说,蛋质量控制至关重要.
- 检测卵子损伤的传统方法往往是低效的.
- 需要自动检测物理损伤,以确保卵子的安全性和质量.
研究的目的:
- 开发一个自动化系统来检测和分类受损的蛋.
- 通过使用深度学习算法来加强蛋质量控制.
- 为了比较不同深度学习模型对卵损伤检测的性能.
主要方法:
- 使用了794张蛋图像的数据集,分类为受损或完好无损.
- 采用了四种深度学习模型:googLeNet,VGG-19,MobileNet-v2和ResNet-50. 这四种深度学习模型包括:
- 训练和评估裂和表面损伤识别模型.
主要成果:
- 谷歌LeNet获得了最高的分类准确率,达到98.73%.
- VGG-19,MobileNet-v2和ResNet-50的准确率分别为97.45%,97.47%和96.84%,这些数据的准确度均为97.45%和96.84%.
- 所有测试的深度学习模型都在检测卵子损伤方面表现出很高的性能.
结论:
- 深度学习,特别是谷歌LeNet模型,提供了一个高度准确和高效的方法,用于自动检测卵损伤.
- 这项技术可以显著改善质量控制,减少产品损失,提高蛋行业的食品安全.
- 自动检测系统为识别受损蛋提供了比传统方法更快,更可靠的替代方案.
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