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AHG-YOLO:在复杂的果园场景中检测封闭的梨果的多类别检测

Na Ma^1,2,3, Yile Sun^1,2, Chenfei Li^1,2

¹College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China.

Frontiers in plant science

|June 9, 2025

概括

这项研究引入了AHG-YOLO模型,用于在果园中快速检测梨果,改进机器人收获. 该模型能够高精度地有效地识别非封闭的,封闭的叶子和与水果接触的水果.

科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
农业技术农业技术

背景情况:

在复杂的果园环境中,快速准确地检测梨果对于优化收获机器人路径规划至关重要.
现有的方法可能会与叶子,树枝或其他水果引起的水果遮作斗争.

研究的目的:

提出AHG-YOLO模型用于多类别检测梨果封闭.
为了提高机器人收获应用的梨子检测效率和准确性.

主要方法:

该研究使用轻量级YOLOv11n模型作为基础,在检测头中结合了ADown downsampling和共享重量参数.
实施了组卷积和通用交叉对联 (GIoU) 损失函数,以提高检测性能和融合速度.
梨根据封闭状态被分类为非封闭 (NO),被叶子/树枝封闭 (OBL) 和水果与水果接触 (FCC).

主要成果:

AHG-YOLO模型获得了高的AP分数:93.5% (FCC),95.3% (NO) 和93.4% (OBL),总的mAP@0.5为94.1%.
与基础YOLOv11n相比,精度,回忆和mAP分别提高了2.5%,3.6%和2.3%.
模型尺寸减少到5.1MB,参数减少了16.9%,表明了轻量级的设计.

结论:

AHG-YOLO模型在复杂的果园环境中检测封闭的梨果方面表现出卓越的性能.
该模型的轻量级和高效性使得它适合在嵌入式设备上部署,用于梨收割机器人.
这项研究为推进自动水果采摘技术提供了宝贵的技术支持.

关键词:

ADown 是自己的.耀的世界这就是YOLOv11的意义.群组卷积的卷积组卷积的卷积对象检测检测对象检测对象检测梨的果实是梨的果实.

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