在电子束接中基于深度学习的接接识别和轨迹规划
Hao Yang1, Congjin Zuo1, Haiying Xu1,2
1China Aviation Manufacturing Technology Research Institute, Beijing 100024, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种增强的混合算法,用于真空电子束接中的接识别,在具有挑战性的条件下提高精度. 这种新方法显著提高了自动接指导的识别性能.
科学领域:
- 材料科学与工程 材料科学与工程
- 机器人和自动化机器人与自动化
- 计算机视觉和图像处理
背景情况:
- 真空电子束接由于黑暗环境和金属反射,给接识别带来了重大挑战.
- 在这些复杂的工业环境中,现有的方法在准确性和实时性能方面扎.
研究的目的:
- 开发一种改进的混合算法,用于真空电子束接中强大的接识别.
- 通过有效的接前视觉指导,增强电子束接的自动化.
主要方法:
- 建议使用混合算法,将YOLOv11-seg与自适应的Canny边缘检测相结合.
- 整合了UFO-ViT注意力机制和EIoU损失功能,用于网络优化.
- 在Canny边缘检测中使用Otsu方法进行自适应值设置.
主要成果:
- 优化模型的平均精度 (mAP) 为77.4%,比基线提高了9%.
- 该系统以每秒20 (FPS) 的速度运行,确保实时处理能力.
- 生成的接轨迹显示,与实际接的平均长度偏差小于3mm.
结论:
- 拟议的混合算法显著提高了复杂条件下的接识别性能.
- 这种方法为预接视觉指导提供了有效的解决方案,推进了电子束接自动化.
- 实时性能和高精度使其适用于工业应用.
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