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YOLO-Granada:一个轻量级的注意力Yolo,用于石榴果实检测果实

Jifei Zhao¹, Chenfan Du¹, Yi Li¹

¹College of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, Henan, China.

Scientific reports

|July 22, 2024

概括

一个新的YOLO-Granada算法改善了智能果园的石榴检测. 这种轻量级模型提供更快的实时检测,尺寸和计算负载减少,有助于农业管理.

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
对于农业的深度学习.

背景情况:

石榴管理依赖于手工劳动和经验,导致效率低下.
智能果园系统需要准确和快速的石榴检测,以提高产量和解决劳动力短缺问题.
现有的深度学习模型在石榴检测中与小目标,大参数和缓慢的计算速度作斗争.

研究的目的:

为智能农业管理开发一种轻量级和高效的石榴检测算法.
与现有的深度学习方法相比,提高石榴检测的准确性和速度.
为Android设备创建一个实时石榴检测应用程序.

主要方法:

一个改进的你只看一次的5版本 (YOLOv5) 算法被提议,命名为YOLO-Granada.
一个轻量级的ShuffleNetv2网络被用作特征提取的骨干.
集成卷积,频道混合和卷积块注意模块 (CBAM) 已集成,以优化检测.

主要成果:

YOLO-Granada算法实现了0.922的平均精度,略低于YOLOv5s (0.929).
该模型显示检测速度和显著压缩增加了17.3%:参数 (54.7%),FLOP (51.3%) 和模型大小 (56.3%).
通过使用Nihui框架开发的Android应用程序,以每秒8.66图像的速度实现了实时检测.

结论:

YOLO-Granada算法提供了一个更准确和轻量级的解决方案,用于实时检测果园中的石榴.
这种方法解决了传统深度学习模型在小目标检测和计算效率方面的局限性.
开发的系统为在农业应用中设计神经网络提供了宝贵的参考,促进了智能果园管理.

关键词:

安卓的部署安卓的部署在CBAM的注意力机制.轻量级网络ShuffleNet 轻量级网络ShuffleNet是一个轻量级网络.石榴的生长周期检测检测这是YOLOv5的.

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