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高精度实例细分检测微米级初级碳化物在基超级合金中的工业应用

Jie Zhang¹, Haibin Zheng¹, Chengwei Zeng¹

¹College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.

Materials (Basel, Switzerland)

|October 16, 2024

概括

本研究提出了一种改进的YOLOv8n模型,用于精确检测基超级合金中的碳化物,增强工业合金的优化. 新模型提供快速,自动化分析,超越传统方法,以更好地表征材料.

科学领域:

材料科学材料科学材料科学
金工业是一种金工业.
计算机视觉计算机视觉

背景情况:

精确识别微米大小的二相,如碳化物,对于优化合金组成和工业流程至关重要.
碳化物检测的传统值细分方法缺乏准确性,效率和客观性.

研究的目的:

开发一个先进的实例细分模型,用于精确和自动检测基超级合金中的碳化物.
通过增强特征融合和小目标检测能力来改进现有的检测方法.

主要方法:

开发了一个增强的YOLOv8n网络,包括SPDConv模块和P2小目标检测层.
该模型应用于各种基于的超级合金,用于碳化物实例细分.
提出了一种用于量化碳化物分散均性的新方法,并将其集成到自动化数据处理框架中.

主要成果:

增强的YOLOv8n模型显示了检测准确度的显著改善,mAP50 (盒子) 从0.676增加到0.828,mAP50 (面具) 从0.471增加到0.644.
拟议的模型在精度,速度和自动化方面优于传统的值细分技术.
综合框架使碳化物预测到分布分析的无自动化成为可能.

结论:

开发的碳化物实例细分模型满足了对精确,快速和批量自动检测的工业要求.
增强的YOLOv8n模型为基于的超级合金的材料表征和工艺优化提供了卓越的解决方案.
该自动化框架有助于对碳化物分布均性的全面分析.

关键词:

碳化物的碳化物.深度学习是一种深度学习.分散的分散性分散.实例细分实例细分实例细分

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