LCGSC-YOLO:基于YOLO框架下的LCNet和GSConv模块的轻量级果叶病检测方法
Jianlong Wang1, Congcong Qin1, Beibei Hou1
1School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.
Frontiers in plant science
|November 15, 2024
概括
这项研究介绍了LCGSC-YOLO,这是一种轻量级的深度学习模型,用于检测果叶病. 它实现了高精度 (95.5% mAP) 和速度 (53 FPS),参数和计算比现有方法少得多.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 目前用于检测植物疾病的深度学习模型是计算密集的.
- 果叶病的识别为自动化系统提供了复杂的场景.
研究的目的:
- 开发一种轻量级,高效的深度学习模型,用于检测果叶病.
- 为了减少模型参数和计算负载,同时保持高精度.
主要方法:
- 该研究提出了LCGSC-YOLO,这是一个修改后的YOLO框架,将LCNet (轻量级CPU卷积神经网络) 作为骨干.
- 在子网络中集成GSConv (组混合卷积) 和VOVGSCSP模块,以实现高效的功能融合.
- 包括协调注意力机制,以减轻模型轻量化导致的准确性损失.
主要成果:
- 在组合的数据集 (植物病理学2021和AppleLeaf9) 上,LCGSC-YOLO实现了95.5%的平均平均精度 (mAP).
- 该模型的检测速度为每秒53 (FPS).
- 与其他算法相比,LCGSC-YOLO显著减少了参数数量和浮点运算 (FLOP).
结论:
- LCGSC-YOLO为检测果叶病提供了一个高效和准确的解决方案.
- 轻量级的设计使其适用于资源有限的环境.
- 该方法解决了模型复杂性和农业应用中的检测性能之间的权衡.
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