一种改进的AUD-YOLO方法用于检测风力轮机叶片的表面损伤
Li Zou1,2, Anqi Chen3, Xinhua Yang4
1School of Intelligent Rail Engineering, Dalian Jiaotong University, Dalian, 116028, China. lizou@djtu.edu.cn.
Scientific reports
|February 18, 2025
概括
这项研究介绍了AUD-YOLO,这是一个改进的YOLOv8模型,用于检测风力轮机叶片 (WTB) 损坏,即使在恶劣的天气中. 该模型提高了检测精度和效率,为轮机维护提供了实用解决方案.
科学领域:
- 可再生能源工程可再生能源工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 风力轮机叶片 (WTB) 损坏检测对于运营效率和寿命至关重要.
- 现有的方法与错误的阳性,错过的检测和恶劣天气 (雾,雪) 相斗争.
- 需要先进的计算机视觉模型来进行强大的WTB损害评估.
研究的目的:
- 开发一个改进的YOLOv8模型 (AUD-YOLO) 来准确检测WTB损坏.
- 在具有挑战性的天气条件下增强特征提取和语义信息捕获.
- 创建一个移动应用程序,用于现场WTB损坏检测.
主要方法:
- 将ADown模块集成到YOLOv8骨干中,以减少参数和改进功能提取.
- 整合UniRepLKNet大型卷积内核与C2f模块进行全面的特征学习.
- 使用轻量级的DySample动态上采样器来丰富语义信息.
主要成果:
- 在复杂的天气条件下,AUD-YOLO在检测WTB损坏方面取得了卓越的性能.
- 与标准YOLOv8.95相比,该模型显示mAP@0.5的改善率为3%,mAP@0.5-0.95的改善率为6.2%.
- 音频YOLO拥有2.5M参数和7.2GFLOP,适用于资源有限的环境.
- 一个移动应用程序,WTBs损坏检测系统,已成功开发.
结论:
- AUD-YOLO为WTB损坏检测提供了高效和高效的解决方案,特别是在不利的条件下.
- 该模型的轻量级设计使其适合在具有有限计算能力的移动设备上部署.
- 开发的移动应用程序促进了现场WTB损害评估的实用性,改善了轮机维护策略.
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