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YOLO-Weld:一个改进的基于YOLOv5的接特征检测网络,用于极端接噪声
Ang Gao1,2, Zhuoxuan Fan1,2, Anning Li1,2
1School of Mechanical Engineering, Shandong University, Jinan 250061, China.
这项研究介绍了YOLO-Weld,这是一个用于在噪音条件下准确检测接特征点的新型网络. 该模型增强了速度和感知,实现了实时接应用的高精度.
科学领域:
- 机器人和自动化机器人与自动化
- 计算机视觉 计算机视觉
- 制造业 制造技术 制造技术
背景情况:
- 准确的接特征点检测对于接轨迹的规划和跟踪至关重要.
- 现有的方法在高噪音的接环境中扎着性能下降.
- 传统的卷积神经网络 (CNN) 方法在极端噪音下面临限制.
研究的目的:
- 开发一个先进的功能点检测网络,用于在具有挑战性,高噪音的工业环境中强大的接定位.
- 与现有方法相比,提高接特征点检测的速度,准确性和稳定性.
- 解决当前方法在极端接噪声条件下的性能瓶问题.
主要方法:
- 拟议的YOLO-Weld网络基于一个改进的You Only Look Once版本5 (YOLOv5).
- 整合了重定格卷积神经网络 (RepVGG) 模块,以优化网络结构和增强检测速度.
- 使用基于正常化的注意模块 (NAM) 来改善特征点感知和轻量级脱头 (RD-Head) 来进行分类和回归准确性.
- 开发了一种接噪声生成方法,以增加极端噪声环境中的模型稳定性.
主要成果:
- 与双阶段检测方法和传统的CNN方法相比,YOLO-Weld模型在定制数据集上表现出更高的性能.
- 在图像中达到2.100像素的平均特征点检测误差,在世界坐标系中达到0.114毫米.
- 该模型满足实时接要求,同时在高噪音环境中准确检测特征点.
结论:
- 拟议的YOLO-Weld网络有效地解决了噪音接条件下现有方法的局限性.
- 整合RepVGG,NAM和RD-Head显著提高了检测速度和准确性.
- 该模型为实际接任务中的特征点检测提供了强大而准确的解决方案,满足了严格的准确性要求.
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