基于DEVA-ConvNeXt模型的复杂背景牛肉新鲜度智能分级方法的研究.
Xiuling Yu1, Yifu Xu1, Chenxiao Qu1
1College of Information and Technology, Jilin Agricultural University, Changchun 130118, China.
Foods (Basel, Switzerland)
|December 30, 2025
概括
一个新的DEVA-ConvNeXt模型使用先进的图像处理改进了牛肉新鲜度分级. 这种方法提高了准确性和速度,使其适合于现实应用和设备设计.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 食品科学 食品科学 食品科学
背景情况:
- 牛肉新鲜度分级面临数据收集,复杂的背景和模型准确性的挑战.
- 现有的方法在多样化和混乱的环境中难以提取特征.
研究的目的:
- 引入一种新的DEVA-ConvNeXt模型,以准确高效地分类牛肉的新鲜度.
- 为了解决当前牛肉图像分析技术的局限性.
主要方法:
- 开发了Alpha-Background Generation Shift (ABG-Shift),用于快速生成具有复杂背景的数据集.
- 集成的动态非局部坐标注意 (DNLC) 和增强的深度卷积 (EDW) 模块,用于优越的特征提取.
- 利用变焦损失 (VFL) 加快学习并改善模型的融合.
主要成果:
- DEVA-ConvNeXt显著超过了ResNet101和ShuffleNet V2.2的表现.
- 与ConvNeXt基线相比,识别准确度 (94.8%),精度 (94.8%),回忆率 (5.9%) 和F1得分 (94.7%) 的提高为6.2%,精度为5.4%,回忆率为5.9%,F1得分为6.0%.
- 已证明在嵌入式设备上进行现实部署的可行性,平衡准确性和速度.
结论:
- DEVA-ConvNeXt模型为牛肉新鲜度分级提供了一个强大的解决方案.
- 提出的技术为开发先进的分级设备提供了宝贵的技术支持.
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