基于改进的MSRCR图像增强算法和YOLOv8模型的变压器排放碳痕迹检测
Hongxin Ji1, Peilin Han1, Jiaqi Li1
1School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
一个新的MSH-YOLOv8算法增强了使用微机器人的内部变压器缺陷检测. 这种方法可以提高图像质量,并准确识别碳痕迹缺陷,提高检查效率.
科学领域:
- 电气工程 电气工程
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 由于金属外,大型变压器的内部缺陷难以视觉检测.
- 现有的检查方法对于复杂的内部环境缺乏效率和准确性.
研究的目的:
- 开发一种基于图像增强和深度学习的先进方法,用于使用微机器人检测内部变压器缺陷.
- 提高在具有挑战性的条件下识别碳痕迹缺陷的准确性和速度.
主要方法:
- 改进的MSRCR算法与白平衡相结合,用于图像增强.
- 通过SimAM机制和DyHead框架来增强YOLOv8深度学习模型,用于特征提取和检测.
- 对YOLOv8应用了模型修剪,以优化检测速度和效率.
主要成果:
- 拟议的MSH-YOLOv8算法实现了91.80%的准确性,比原来的YOLOv8.4有3.4%的改进.
- 该算法显示每秒 (FPS) 为99.2,表明计算复杂性降低.
- 该方法显著优于其他主流目标检测算法.
结论:
- MSH-YOLOv8算法有效地提高了内部变压器缺陷检测的准确性和速度.
- 开发的方法适用于使用变压器内部检查机器人的实际工程应用.
- 这种方法为电力基础设施的非破坏性测试提供了一个强大的解决方案.
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