一个混合ResNet50视觉变压器模型,具有用于空中图像分类的注意力机制
Amr Aboghanem1, Mohamed Abd Elfattah2, Hanan M Amer3
1Electronics and Communications Department, Faculty of Engineering, Mansoura University, Mansoura University, Mansoura, 35516, Egypt. Amraboghanem836@gmail.com.
Scientific reports
|February 10, 2026
概括
本研究介绍了空中图像分类的两种混合模型,解决了ResNet-50和视觉变压器 (ViT) 功能的复杂性. 多头注意力模型实现了95.80%的准确性,超过了现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 由于图像的复杂性和可变性,空中图像的分类带来了重大挑战.
- 现有的方法很难有效地处理空中图像中存在的各种特征.
研究的目的:
- 提出和评估两种新的混合深度学习模型,用于增强空中图像分类.
- 通过利用先进的特征提取和注意力机制来解决空中图像分类的公开挑战.
主要方法:
- 使用ResNet-50和视觉变压器 (ViT) 进行特征提取.
- 开发两种混合模型:一种是多头注意力 (MHA),另一种是交叉注意力.
- 用精度,回忆,F1分数和ROC曲线对锡金空中图像对象检测数据集 (SAIOD) 的评估.
主要成果:
- 结合ResNet-50,ViT和MHA的混合型号实现了95.80%的卓越精度.
- 两种拟议的混合型号都表现出性能超过当前最先进的方法.
- 基于MHA的模型在识别信息特征以进行分类方面表现出特别强大的能力.
结论:
- 混合型号将ResNet-50和ViT功能与注意力机制相结合,为空中图像分类提供了强大的解决方案.
- 基于MHA的方法对于提高复杂空中图像的分类准确性和特征选择非常有效.
- 拟议的模型代表了航空图像分析和物体检测领域的重大进步.
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