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一个增强的YOLOv8n物体探测器用于合成钻石质量评估

Shixiong Zhang¹, Ang Li², Jianxin Ren²

¹School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou, China. cehon@126.com.

Scientific reports

|November 14, 2024

概括

这项研究介绍了YOLOv8n-adamas,这是一种改进的AI模型,用于自动化合成钻石质量分类. 它增强了检测和评估,为制造应用提供了更好的精度和回忆.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
材料科学材料科学材料科学

背景情况:

合成钻石的自动化质量分类对于制造至关重要.
现有的方法在实时评估中缺乏效率和准确性.

研究的目的:

为自动化合成钻石检测和质量评估开发一个增强的人工智能模型.
为了提高工业环境中合成钻石分类的精度和回忆力.

主要方法:

开发了一个专门的合成钻石数据集.
通过重新设计脊柱和引入具有注意力机制的动态检测头来改进YOLOv8n模型,从而创建了YOLOv8n-adamas.
使用精度,回忆和平均平均精度 (mAP) 指标评估模型性能.

主要成果:

与YOLOv8.8相比,YOLOv8n-adamas的精度提高了4.0%,回忆能力增加了2.7%.
在平均精度方面实现了1.5% (mAP50) 和1.3% (mAP95) 的改进.
在合成钻石数据集中表现优于其他高性能模型.

结论:

YOLOv8n-adamas为自动化,基于质量的合成钻石分类提供了有效的技术支持.
增强型号为合成钻石检测和评估提供了卓越的性能.
这一进步促进了更高效,更准确的工业分类过程.

关键词:

这就是ConvNeXtV2的原因.动态头部动态头部质量评估质量评价质量评价合成钻石是一种合成钻石.这就是YOLOv8n.

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