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基于FastQAFPN-YOLOv8s的方法,用于快速轻量检测核桃未分离材料

Junqiu Li¹, Jiayi Wang², Dexiao Kong²

¹College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.

Journal of imaging

|December 27, 2024

概括

这项研究介绍了FastQAFPN-YOLOv8s,这是一个高效的对象检测网络,用于分类核桃. 该模型大大减少了尺寸和训练时间,同时保持了高精度,改善了自动化生产.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
农业技术农业技术

背景情况:

核桃是有价值的作物,但高效的自动分类和核是具有挑战性的.
现有的物体检测模型可能无法满足实时自动分类系统的速度和尺寸要求.

研究的目的:

开发一个轻量级和快速的物体检测网络,用于精确识别未分类的核桃.
通过快速准确的外和内核检测来提高核桃自动化加工的效率.

主要方法:

提出了一个新的FastQAFPN-YOLOv8s网络,利用轻量级的Pconv运营商和FasterNextBlock骨干.
集成了一个ECIoU损失函数,用于更快的网络回归和预测框架调整.
采用QAFPN特征融合网络与Rep-PAN结构,以实现高效的特征提取和融合.

主要成果:

该FastQAFPN-YOLOv8s模型实现了94.5%的平均精度 (mAP) 在每秒52.1 (FPS).
与原始YOLOv8s.s.相比,在参数 (45.5%),训练时间 (32.7%) 和模型大小 (12.3 MB) 中显著减少.
在模型尺寸缩小和FPS方面表现优于YOLOv7和YOLOv6,同时保持了可比或改进的准确性.

结论:

FastQAFPN-YOLOv8s网络提供了一种有效的解决方案,可以减少核桃分类中的模型大小和推断时间.
拟议的轻量级设计平衡了性能和速度,使其适合实时自动化农业应用.
这一进步有助于在坚果行业中实现更高效,更准确的自动化加工.

关键词:

快速QAFPN-YOLOv8s 的时间 QAFPNQAFPNQAFPN QAFPNQAFPN QAFPNQAFPN QAFPNQAFPN QAFPNQAFPN QAFPNQAFPN QAFPNQ QAFPNQ 轻量级的轻量级的轻量级的轻量级的对象检测检测对象检测对象检测核桃分类核桃分类

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