激光切割多晶立方化工具的表面缺陷检测模型基于非对称的融合策略
Anfu Zhu1, Jiaxiao Xie2, Heng Guo2
1North China University of Water Resources and Electric Power, Zhengzhou, 450045, China. zhuanfu@ncwu.edu.cn.
Scientific reports
|November 4, 2024
概括
这项研究引入了一种改进的AFFS-YOLO模型,用于检测激光切割多晶立方化工具的表面缺陷. 新模型提高了准确性和实时检测能力,解决了传统方法的局限性.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造业 制造技术 制造技术
- 计算机视觉和机器学习
背景情况:
- 多晶立方化工具在激光切割过程中容易出现缺陷,因为它们的硬度和脆性.
- 传统的缺陷检测方法缺乏现代制造所需的准确性和实时性能.
研究的目的:
- 为激光切割多晶立方化工具开发先进的表面缺陷检测模型.
- 与现有方法相比,提高检测准确度和实时性能.
主要方法:
- 提出了一个AFFS-YOLO模型,在骨干中包含一个C3SE模块,用于增强特征提取.
- 在部实施了一个非对称的特征金字塔网络 (AFPN),以改善多层次信息融合.
- 在头部引入了一种软抑制机制,以减少重叠的检测和提高准确性.
主要成果:
- AFFS-YOLO模型的平均精度为86.1%,比YOLOv5.5有5.6%的改进.
- 与原始网络和其他经典物体检测网络相比,其检测性能明显优越.
- 对激光切割缺陷的自构建数据集进行了实验验证.
结论:
- 拟议的AFFS-YOLO模型有效地解决了在激光切割多晶立方化工具中检测表面缺陷的挑战.
- 非对称的融合策略显著提高了检测准确性和实时能力.
- 这项研究填补了先进切削工具自动缺陷检测的关键缺口.
相关概念视频
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