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基于改进的ConvNeXt-T,对的蛋黄进行非破坏性测试
Haoran Chen1, Yulong Chang1, Yuanzhe Chen1
1College of Engineering, Huazhong Agricultural University, Wuhan, Hubei, China.
Journal of food science
|May 9, 2024
概括
研究人员开发了一种增强的ConvNeXt-T模型,用于检测未经制的蛋黄. 改进后的模型大大降低了参数,提高了准确性,有助于食品行业的质量检查.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 盐蛋黄是食品工业的重要成分.
- 制不足的黄是液态的,容易腐烂,需要检测.
- 目前的检测方法可能缺乏效率.
研究的目的:
- 开发一个准确和高效的AI模型来分类蛋黄.
- 为此具体任务改进ConvNeXt-T基准模型.
- 为了提高蛋黄生产的质量控制.
主要方法:
- 建立了一个专门用于蛋黄的数据集.
- 修改了ConvNeXt-T模型,将位置感知圆形卷积 (ParC-block) 纳入.
- 将原始ConvNeXt-T块的部分替换为ParC块,并修改了激活功能.
主要成果:
- 增强的模型,ParC-ConvNeXt-SMU-T,在定制数据集上显示了更快的趋同.
- 模型参数减少了4的因素.
- 在2680万个参数的测试集中获得了96.833%的准确性,提高了2.167%.
结论:
- 改进的模型可以有效地识别制的蛋黄,特别是未经制的蛋黄.
- 这种人工智能方法在食品生产质量检查中提供了实际应用.
- 提高排序效率和降低蛋行业劳动力成本的潜力.
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