热交换器管道孔识别使用改进的YOLOv5模型,集成双重注意力机制
Ruijun Yang1, Yining Zhu1, Bin Xu1
1School of Intelligent Technology-School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, 201418, China.
Scientific reports
|January 10, 2026
概括
这项研究介绍了DANet-YOLOv5,这是一个先进的AI模型,用于识别热交换器管口. 它显著提高了检测准确度,这对于自动化工业清洁机器人至关重要.
科学领域:
- 工业工程 工业工程 工业工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 换热器是重要的工业设备,需要有效的清洁方法.
- 目前用于管口识别的视觉算法存在检测不准确性,例如错过或错误的阳性.
- 自动化清洁解决方案对于工业效率和安全越来越重要.
研究的目的:
- 开发一种改进的视觉算法,用于准确识别热交换机管口.
- 为了克服现有的检测模型的局限性,提高工业清洁的自动化.
- 引入DANet-YOLOv5模型,以提供卓越的管口检测性能.
主要方法:
- 开发了一个改进的YOLOv5模型,DANet-YOLOv5 (双重注意网).
- 双重注意力机制组件被集成到骨干网络中.
- 采用了二次注意力聚合和特征适应分布等技术.
主要成果:
- 与现有的算法和YOLOv8.5相比,DANet-YOLOv5表现出了更高的性能.
- 关键指标包括错误率,召回率和mAP值显示显著改善.
- 该模型提高了换热器管口检测的准确性和稳定性.
结论:
- 在工业环境中,DANet-YOLOv5模型提供了一个强大的解决方案,用于在工业环境中准确识别管口.
- 这一进步是开发和部署自动化清洁机器人的基础.
- 改进的检测能力为更高效和更智能的工业维护铺平了道路.
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