一种基于YOLOv5和多尺度数据增强的新方法,用于变电站视觉检查
Junjie Chen1, Siqi Pan2, Yanping Chan2
1State Grid Zhangzhou Power Supply Company, Zhangzhou, 363000, Fujian, China. 755530740@qq.com.
Scientific reports
|April 23, 2024
概括
本研究引入了混合修剪YOLOv5和多尺度数据增强方法,以改进基于人工智能的变电站缺陷检测,提高动态环境中的准确性和稳定性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 显示出对变电站视觉检查和缺陷检测的承诺.
- 由于动态的拍摄环境和有限的数据集,实际应用面临着挑战,导致准确性和稳定性低于最佳.
- 现有的方法很难在复杂的变电站设置中始终识别缺陷.
研究的目的:
- 为了提高基于人工智能的变电站缺陷检测的准确性和稳定性.
- 为了解决动态拍摄环境和数据不足所带来的局限性.
- 提出一种结合先进数据增强和模型优化的新方法.
主要方法:
- 开发了一种增强的多尺度数据增强技术,以提高在时间变化的条件下识别精度.
- YOLOv5 (你只看一次版本5) 用于使用多尺度图像数据检测缺陷.
- 实施了一种新的模型修剪方法,以战略性地减少YOLOv5参数,提高模型的稳定性和准确性.
主要成果:
- 拟议的增强多尺度数据增强有效地减轻了环境变化.
- 混合修剪YOLOv5显著提高了缺陷识别准确性和模型稳定性.
- 对变电站缺陷图像的实验结果验证了该方法的有效性.
结论:
- 混合修剪YOLOv5和多尺度数据增强方法为变电站缺陷检测提供了一个强大的解决方案.
- 这种方法可以在具有挑战性的现实变电站环境中提高AI性能.
- 这些发现有助于为关键基础设施提供更可靠,更准确的自动化视觉检查系统.
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