基于改进的MobileNetV2的农产品分类和识别
Haiwei Chen1, Guohui Zhou2, Wei He1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Scientific reports
|February 11, 2024
概括
这项研究使用增强的MobileNetV2模型改进了农产品分类. 新的Res-Inception和EMA模块提高了精准农业应用的准确性.
科学领域:
- 农业自动化和情报 农业自动化和情报
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 越来越多的生产效率需求推动了农业自动化.
- 精确的分类模型对于识别和加工农产品至关重要.
- 现有的MobileNetV2模型显示在农业子类别的识别中存在认可偏差.
研究的目的:
- 提高农产品分类的准确性.
- 为了解决MobileNetV2模型的识别偏差.
- 通过更好的分类,提高农产品的效率和经济价值.
主要方法:
- 开发了一个改进的MobileNetV2卷积神经网络.
- 引入了一种新的Res-Inception模块,将Inception和剩余模块结合起来.
- 将一个高效的多尺度跨空间学习模块 (EMA) 集成到网络骨干中.
主要成果:
- 改进的MobileNetV2模型在Fruit-360数据集上表现出卓越的性能.
- 与原来的MobileNetV2.2相比,实现了1.86%的精度增加.
- 拟议的修改有效地解决了农产品子类别的认可偏差.
结论:
- 增强的MobileNetV2模型为农产品分类提供了更高的准确性.
- 新的Res-Inception和EMA模块有助于提高检测和分类性能.
- 这种进步支持精准农业,并最大限度地提高了农产品的经济价值.
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