YOLOv8s-Longan:一种轻量级的检测方法,用于长安果摘无人机
Jun Li1,2,3, Kaixuan Wu1, Meiqi Zhang1
1College of Engineering, South China Agricultural University, Guangzhou, China.
Frontiers in plant science
|February 6, 2025
概括
一个新的轻量级深度学习算法,YOLOv8s-Longan,增强无人机的水果检测. 这种人工智能模型提高了3.9%的准确性,同时降低了20.3%的参数,使得水果采摘更快,更精确.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 无人驾驶飞行器 (UAV) 需要高效的算法来快速检测水果,因为机载计算能力有限,飞行速度高.
- 精确的水果定位对于自动水果采摘操作至关重要.
研究的目的:
- 开发一种轻量级的深度学习算法 (YOLOv8s-Longan),用于在无人机应用中增强果实检测.
- 为了提高检测准确度,并减少实时采摘水果的模型参数.
主要方法:
- 整合一个平均值和最大值聚合注意力 (AMA) 模块到DenseAMA和C2f-Faster-AMA模块中,以实现网络轻量化和通用化.
- 实现一个VOVGSCSPC模块用于多尺度特征融合,以增强图像理解.
- 采用一种新的Inner-SIoU损失函数来改进目标界限框回归.
主要成果:
- 在复杂的环境中,YOLOv8s-Longan算法实现了84.3%的平均平均精度 (mAP@0.5),用于检测密集和封闭的龙果.
- 与其他YOLOv8模型相比,在mAP@0.5中显示出3.9%的改进.
- 在模型参数中实现了20.3%的减少,有助于更快的处理.
结论:
- YOLOv8s-Longan算法满足了在无人机采摘系统中检测水果的高精度和速度要求.
- 拟议的轻量级模型为农业机器人实时水果识别提供了实际解决方案.
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