基于改进的轻量级神经网络的花图像分类,具有多尺度特征融合和注意力机制
Zhigao Zeng1,2, Cheng Huang1,2, Wenqiu Zhu1,2
1School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了用于花图像分类的轻量级深度学习模型. 新型号减少了功能损失和存储空间,提高了移动设备的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对于花分类的深度学习模型,在早期阶段经常会丢失特征信息.
- 现有的模型可以很大,消耗大量的存储空间,限制移动应用.
研究的目的:
- 开发一种轻量级的神经网络,用于花的图像分类.
- 为了增强特征提取和减少模型大小用于移动部署.
主要方法:
- 一个基于AlexNet的新型轻量级模型,包含一个多尺度特征融合模块 (MFFM).
- 多元金融金融市场使用深度可分离卷积来进行多尺度特征融合,减少早期特征损失.
- 整合了改进的Inception模块和混合注意力机制,以增强深度特征提取并专注于关键信息.
主要成果:
- 与基线相比,拟议的轻量级模型显示了较少的参数和较少的存储要求.
- 实现了比基线模型更高的分类准确度.
- 该模型适合在移动设备上准确识别花朵图像.
结论:
- 开发的轻量级神经网络有效地解决了花图像分类中的特征丢失和存储问题.
- 多个尺度的特征融合和注意力机制的结合导致了更高的准确性和效率.
- 该模型为设备上花朵识别应用提供了可行的解决方案.
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