接接跟踪和检查机器人基于改进的YOLOv8s-Seg模型
Minghu Zhao1, Xinru Liu2, Kaihang Wang1
1School of Science, Xi'an Polytechnic University, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种高效的接接跟踪和检查机器人,使用轻量级的YOLOv8s-seg模型. 机器人实现了高精度 (97.8%) 和实时性能,提高了特殊设备的安全性.
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
- 材料科学 材料科学 材料科学
背景情况:
- 接是特殊设备中的关键但脆弱的部件,需要强大的检查方法.
- 传统的接检查是昂贵的,耗时的,劳动密集型的,阻碍了效率和安全.
- 接接跟踪和检查机器人等自动化解决方案提供了显著的改进.
研究的目的:
- 开发一个高效准确的接接跟踪和检查机器人.
- 优化YOLOv8s-seg模型以减少尺寸和提高接检测性能.
- 为了实现实时路径规划,用于机器人接检查.
主要方法:
- 使用了YOLOv8s-seg与MobileNetV3轻量级的骨干来减少模型参数.
- 在C2fGhost模块中重建C2f和修剪道以进一步优化.
- 集成了一个EMA注意力机制,以弥补轻型模型中的精度损失.
- 将优化的模型嵌入到Jetson Nano系统中,并使用TensorRT进行加速推理.
主要成果:
- 实现了97.8%的接识别精度,模型大小仅为4.88 MB.
- 展示了54ms的总推理时间,用于图像细分到路径拟合.
- 成功启用实时跟踪和检测,满足机器人的操作要求.
- 实现了机器人接检查的高效准确路径规划.
结论:
- 拟议的基于YOLOv8s-seg的轻量级机器人显著提高了接检查的效率和准确性.
- 优化的模型和实时处理能力满足了机器人跟踪和检测的需求.
- 这种方法为提高特殊设备安全提供了具有成本效益和可靠的解决方案.
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