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DKTransformer:精细粒度食品图像分类的准确高效模型
Hongjuan Wang1, Chenxi Wang1, Xinjun An1
1College of Intelligent Equipment Affiliation, Shandong University of Science and Technology, Tai'an 271019, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
新的混合型号DKTransformer通过结合视觉变压器和CNN来增强食品图像分类. 这种方法提高了详细的食品识别任务的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 食物图像分类对于饮食分析和健康计算至关重要.
- 细粒度食品类别由于视觉相似性和类内变化而存在挑战.
- 现有的方法通过过度依赖全球或本地特征来应对复杂的食物场景.
研究的目的:
- 提出DKTransformer,一个轻量化混合架构用于细粒度食品图像分类.
- 为了增强本地细节建模,并有效地捕获远程依赖.
- 为了减少食品图像分类模型中的计算成本和参数冗余.
主要方法:
- 开发了DKTransformer,集成视觉转换器 (ViT) 和卷积神经网络 (CNN).
- 引入了使用深度可分离卷积的局部细节提取 (LDE) 模块.
- 设计了一个多尺度扩展注意力 (MSDA) 模块,用于高效的远程依赖模型.
- 采用高效的Kolmogorov-Arnold网络 (EfficientKAN) 取代传统的前网络.
主要成果:
- 在47M参数的ETH Food-101上,DKTransformer实现了92.71%的Top-1精度.
- 在Vireo-Food-172上达到90.70%的Top-1精度,在ISIA Food-500上达到66.89%.
- 在各种食品数据集和尺度上表现出强烈的概括性.
- 展示了分类准确性和计算效率之间的有利平衡.
结论:
- DKTransformer有效地解决了细粒度食品图像分类的挑战.
- 混合架构为准确和高效的食品识别提供了一个有希望的解决方案.
- 该方法显示出在饮食分析和健康计算中的应用潜力很大.
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