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最小YOLO:基于YOLOv8的显微镜下矿物成分的轻量级识别方法

Zeyang Qiu¹, Xueyu Huang^1,2, Xiangyu Xu¹

¹School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

一种新的轻量级方法,Minim-YOLO,可以在边缘设备上快速识别矿物质. 这种计算机视觉方法在显著减少计算资源的情况下实现了高精度,支持智能采矿操作.

科学领域:

地质工程是地质工程.
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

智能采矿依赖于高效的矿物识别来进行矿石分类.
目前的方法可能不适合在资源有限的边缘计算设备上部署.

研究的目的:

为边缘设备开发一种轻量级和高效的矿物识别方法.
为了在智能采矿应用中实现快速矿物分类.

主要方法:

提出了基于 YOLOv8.8 的轻量级模型 Minima-YOLO.
推出了YOLOv8微型,更快的EMA模块与PConv和EMA注意力,GhostConv和Slim-Neck结构.
利用可见光显微镜对矿物质图像数据进行分析.

主要成果:

在自建的矿物数据集上实现了99.4%的mAP50.
将FLOP降低到2.3G,参数降低到0.72M,模型大小降低到1.63MB.
保持了103 FPS的高推断速度.

结论:

最小YOLO为矿物识别提供了高效,轻量级的解决方案.
与其他物体检测方法相比,该算法表现出卓越的性能.
为快速矿物分类提供智能,环保的计算机视觉方法.

关键词:

这就是YOLOv8的意义.边缘设备的设备是边缘设备.莱皮多利特 (Lepidolite) 是一种石头.轻量级网络轻量级的网络.矿物质检测矿物质检测的方法

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