电子测量与仪器学报

刘鹏,袁啸林,侯吉磊,万国扬,江明,周阳,左桂忠.真空检漏机器人目标识别技术研究[J].电子测量与仪器学报,2023,37(8):128-135

真空检漏机器人目标识别技术研究

Vacuum leak detection robot target recognition technology research

DOI：

中文关键词: 聚变装置真空检漏机器人 GhostNetV2 Ghost 卷积注意力机制

英文关键词:fusion device vacuum leak detection robot GhostNetV2 GhostConv attention mechanism

基金项目:国家自然科学基金青年基金(11905254,12105322)、安徽省自然科学基金青年基金(2108085QA38)项目资助

作者	单位
刘鹏	1. 安徽工程大学高端装备先进感知与智能控制教育部重点实验室，2. 中国科学院等离子体物理研究所
袁啸林	2. 中国科学院等离子体物理研究所
侯吉磊	2. 中国科学院等离子体物理研究所
万国扬	1. 安徽工程大学高端装备先进感知与智能控制教育部重点实验室
江明	1. 安徽工程大学高端装备先进感知与智能控制教育部重点实验室
周阳	1. 安徽工程大学高端装备先进感知与智能控制教育部重点实验室，2. 中国科学院等离子体物理研究所
左桂忠	2. 中国科学院等离子体物理研究所

Author	Institution
Liu Peng	1. Key Laboratory of Advanced Perception and Intelligence Control of High-End Equipment, Anhui Polytechnic University，2. Institute of Plasma Physics, Chinese Academy of Science
Yuan Xiaolin	2. Institute of Plasma Physics, Chinese Academy of Science
Hou Jilei	2. Institute of Plasma Physics, Chinese Academy of Science
Wan Guoyang	1. Key Laboratory of Advanced Perception and Intelligence Control of High-End Equipment, Anhui Polytechnic University
Jiang Ming	1. Key Laboratory of Advanced Perception and Intelligence Control of High-End Equipment, Anhui Polytechnic University
Zhou Yang	1. Key Laboratory of Advanced Perception and Intelligence Control of High-End Equipment, Anhui Polytechnic University，2. Institute of Plasma Physics, Chinese Academy of Science
Zuo Guizhong	2. Institute of Plasma Physics, Chinese Academy of Science

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中文摘要:

在聚变装置真空检漏领域中,未来聚变装置涉氚运行,检漏人员无法进入装置检漏,这使得这项任务极其困难和耗时。为实现聚变装置泄漏设备的快速准确检测,本文以 6 自由度机械臂为研究对象,提出了一种 GV2-YOLOv5 的真空设备检测方法用于真空检漏机器人对真空设备进行识别和定位喷氦。在该方法中,结合轻量级 GhostNetV2 网络构建 C3GhostV2 模块,同时使用轻量的 Ghost 卷积提取目标特征,从而降低模型参数量,提高计算速度;在特征融合网络中添加 Bottleneck Transformers 和 ECA 注意力机制,提高网络特征提取能力以及加强模型通道特征。实验结果表明,在自制数据集上,改进后的模型平均精度为 93. 2%,相比 YOLOv5s 提高了 1. 4%,模型参数量减少了 29. 5%,检测速度为 92 fps,满足实时性与准确性的需求,为真空检漏机器人目标识别与定位提供了一种的解决方案。

英文摘要:

In the field of vacuum leak detection in fusion devices, the future fusion devices are operated with tritium and the leak checkers do not have access to the devices for leak checking, which makes this task extremely difficult and time-consuming. In order to realize the fast and accurate detection of fusion device leakage equipment,and realize the fast and accurate detection of fusion device leakage equipment, this paper takes the six-degree-of-freedom robotic arm as the research object, and proposes a GV2-YOLOv5 vacuum equipment detection method for vacuum leakage detection robots to identify and locate the vacuum equipment for helium injection. In this method, the C3GhostV2 module is constructed by combining lightweight GhostNetV2 network, while using lightweight GhostConv to extract target features, thus reducing the number of model parameters and improving the computational speed. Bottleneck Transformers and ECA Attention mechanism are added to the feature fusion network to improve the network feature extraction capability and to enhance the model channel features. The experimental results show that the average accuracy of the improved model is 93. 2% on the homemade dataset, which is 1. 4% higher than YOLOv5s, the amount of model parameters is reduced by 29. 5%, and the detection speed is 92 fps, which meets the requirements of real-time and accuracy, and provides a solution for the vision localization technology of vacuum leak detection robot.

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