WeldLight:一种轻量级的接分类和特征点提取模型,用于接接跟踪
Ang Gao1,2,3, Anning Li1,2,3, Fukang Su1,2,3
1School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
一个新的卷积神经网络WeldLight通过克服图像噪声和计算需求,精确地跟踪接接. 这种轻量级系统可提高工业视觉应用的准确性和实时性能.
科学领域:
- 机器人和自动化 机器人和自动化
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的基于视觉的接跟踪系统与强烈的图像噪声和高计算成本作斗争.
- 准确的接接分类和特征点定位对于自动化接过程至关重要.
研究的目的:
- 开发一种轻量级,耐噪声的卷积神经网络 (CNN),用于精确的接接特征点分类和定位.
- 提高基于视觉的接跟踪系统在杂环境中的适应性和稳定性.
主要方法:
- 提出了WeldLight,这是一个单阶段轻量级CNN,结合了在线数据增强方法以适应噪声.
- 实现了一个注意模块来过受噪声损坏的功能,提高系统稳定性.
- 利用单线结构光视觉来检测接特征点.
主要成果:
- 在调整的测试套件上获得0.9668的F1分数,用于接分类.
- 证明了低平均绝对定位误差:1.639像素 (低噪音) 和1.736像素 (高噪音).
- 在CPU平台上推断时间为29.32毫秒,满足实时要求.
结论:
- WeldLight为基于视觉的接跟踪提供了强大而高效的解决方案,有效处理强烈的图像噪声.
- 拟议的网络满足工业跟踪应用的实时性能需求.
- WeldLight 提高了在分类和定位接接特征点上的精度和稳定性.
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