通过转移学习与YOLOv8进行磨砂带研磨的火花图像检测研究
Jian Huang1,2, Guangpeng Zhang1
1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种新的多视图火花图像检测方法,使用YOLOv8转移学习来提高刀片抛光精度. 该方法显著提高了工业监控的检测精度和效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 制造业 制造技术 制造技术
背景情况:
- 手动的刀片抛光依赖于经验,导致精度和效率较低.
- 需要自动化方法来智能预测材料去除率.
研究的目的:
- 开发一种多视图火花图像检测方法,以改善刀片抛光.
- 为了提高自动化刀片加工过程的精度和效率.
主要方法:
- 提出了一种使用YOLOv8转移学习的多视图火花图像检测方法.
- 构建了一个具有前方,侧面和45°视图的多功能火花图像数据集.
- 实施了交叉视图转移学习框架,其中包括动态框优化,注意力引导特征对齐和课程学习.
主要成果:
- 在多视图数据集上实现了98.7%的平均检测准确度,比原始YOLOv8.2改进了14.2%.
- 在NVIDIA RTX 4090上,推断速度达到55 FPS,适合工业在线监控.
- 首次证明了成功的交叉视图检测.
结论:
- 拟议的方法显著提高了刀片抛光的精度和效率.
- 该研究为精密加工中的智能预测提供了关键的技术支持.
- 该方法显示了在自动化制造中快速工业部署的潜力.
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