绝缘体缺陷检测基于ML-YOLOv5算法
Tong Wang1,2, Yidi Zhai1,2, Yuhang Li1,2
1Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了ML-YOLOv5,这是一种用于检测绝缘体缺陷的改进算法. 它大大降低了计算成本,同时保持了实时检查的高精度和速度.
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:
- 目前的绝缘体缺陷检测方法在平衡精度,速度,计算参数和FLOP方面面临挑战.
- 高空绝缘体检需要高效准确的自动缺陷检测系统.
研究的目的:
- 开发一个增强的绝缘体缺陷检测算法,优化速度和准确性,同时降低计算负载.
- 提高深度学习模型的效率和适用性,以实时检查高空绝缘体.
主要方法:
- 基于YOLOv5网络提出的ML-YOLOv5算法.
- 在骨干中内置了深度可分离的卷积和改进的C2f_DG模块用于特征融合.
- 改进了多尺度特征金字塔网络 (MFPN),并利用YOLOv5m作为教师模型的知识蒸.
主要成果:
- 实现了参数数量的46.9%的减少和FLOP的43.0%的减少.
- 保持每秒 (FPS) 的63.6,表明高检测速度.
- 在CPLID和IDID数据集上证明了良好的准确性和检测速度.
结论:
- ML-YOLOv5算法有效地平衡了绝缘体缺陷检测的准确性和速度.
- 拟议的改进使得该算法适合实时高空绝缘体缺陷检查.
- 这项工作有助于在电力行业更有效,更实用的自动化视觉检查系统.
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