LWheatNet:一种轻量级的卷积神经网络,具有用于小麦种子分类的混合注意力机制
Xiaojuan Guo1, Jianping Wang1, Guohong Gao1
1School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Frontiers in plant science
|January 27, 2025
概括
一个新的轻量级深度学习模型,LWheatNet,准确地对高效率的小麦种子品种进行分类. 该模型提供了在资源有限的设备上实时图像分析的解决方案.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 农业中的深度学习为作物分类提供了新的方法.
- 现有的深度学习模型面临诸如处理速度慢,计算要求高,小麦种子图像分析准确度低等挑战,阻碍实时应用.
研究的目的:
- 开发一种轻量级的小麦种子分类模型,克服现有的深度学习方法的局限性.
- 为了增强特征表示和提取,以准确识别小麦种子.
主要方法:
- 拟议的LWheatNet模型将混合注意模块与堆叠的倒置剩余卷积网络集成在一起.
- 混合注意力机制结合了并行通道和空间注意力,以改善特征表示.
- 堆叠的倒置残余网络利用深度可分离的卷积,频道混合和频道分割来有效地提取特征,最大限度地减少模型大小和计算负载.
主要成果:
- 在比较模型 (AlexNet,VGG16,MobileNet V2,MobileNet V3,ShuffleNet V2) 中,LWheatNet获得了最高的性能.
- 在只有1.33M的模型尺寸的测试组中获得了98.59%的准确性.
- 与传统的卷积神经网络 (CNN) 和其他轻量级网络相比,其表现优越.
结论:
- LWheatNet为小麦种子图像提供了高识别精度,同时需要最小的存储空间.
- 该模型适用于低性能设备的实时分类和识别任务.
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