一个集成和强大的视觉系统用于内部和外部线程缺陷检测与对抗防御
Liu Fu1, Leqi Li1, Gengpei Zhang1
1The School of Electronic Information and Electrical Engineering, Yangtze University, East Campus, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一个强大的视觉系统,用于工业线程缺陷检测. 它使用先进的成像,生成数据增强和轻量级的深度学习模型进行准确和安全的检查.
科学领域:
- 工业自动化 工业自动化
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于复杂的几何形状和隐藏的微缺陷,难以检测螺纹组件中的缺陷.
- 现有的方法在稳定性,有限的数据和对抗性攻击方面扎.
研究的目的:
- 开发一个集成的,强大的视觉系统来检查内部和外部线程.
- 通过使用先进的AI技术,提高缺陷检测的准确性和效率.
- 确保系统可靠性,防止对抗性干扰.
主要方法:
- 一个统一的成像平台,用于同步线程表面捕获.
- 对于运动模糊和低光条件的高级图像增强.
- 生成数据增强以解决有限的缺陷样本.
- 一个轻量级,优化的深度学习模型用于缺陷检测.
- 对抗对方攻击的双重防御机制.
主要成果:
- 该系统在检查内部和外部线程时实现了高度的稳定性.
- 先进的技术可以提高图像清晰度,克服数据限制.
- 优化的深度学习模型在精度和效率方面超过了现有的YOLO变体.
- 双防御机制有效地减轻了对手的干扰,确保了可靠性.
结论:
- 拟议的框架提供了准确,安全和高效的线程缺陷检测.
- 这为可靠的工业视觉系统提供了切实可行的解决方案.
- 集成先进的成像和人工智能增强了制造业的质量控制.
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