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棉花病的压缩识别网络与现场适应性知识蒸

Xinwen Zhang¹, Quan Feng¹, Dongqin Zhu¹

¹School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou, China.

Frontiers in plant science

|October 11, 2024

概括

这项研究包括使用知识蒸在边缘设备上识别棉花疾病的大型深度网络. 最好的方法实现了90.59%的准确性,同时大大降低了实际部署的计算负载.

科学领域:

农业科学农业科学
计算机科学计算机科学
机器人技术机器人技术机器人技术

背景情况:

深度网络对于农业疾病识别至关重要,但对于边缘设备来说太大了.
资源有限的边缘计算为部署植物保护机器人带来了挑战.
有效的部署需要模型压缩技术.

研究的目的:

在边缘设备上压缩深度网络以识别棉花疾病.
为了减少网络参数和计算复杂性.
在资源有限的机器人上实现深度学习模型的实际部署.

主要方法:

运用知识蒸来压缩大型深度网络.
在教师网络 (DenseNet40),学生网络 (ShuffleNetV2) 和蒸算法 (NST) 上进行了比较实验.
基于识别精度和计算复杂性 (FLOP) 评估模型性能.

主要成果:

使用NST算法的DenseNet40 (教师) 和ShuffleNetV2 (学生) 的最佳组合实现了90.59%的识别精度.
减少了计算复杂性 (FLOP) 从0.29G降至0.045G.
在边缘部署的深度模型中成功证明了轻量化.

结论:

知识蒸有效地压缩了在边缘设备上识别棉花疾病的深度网络.
提出的方法为在资源有限的机器人上部署准确的深度学习模型提供了实际解决方案.
在高识别精度和降低计算成本之间取得了平衡.

关键词:

棉花疾病棉花疾病深度学习是一种深度学习.知识的蒸知识的蒸.一个模型的压缩压缩.现场适应性的现场适应性

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