CLC number:
On-line Access: 2022-06-22
Received: 2022-01-09
Revision Accepted: 2022-05-23
Crosschecked: 2022-09-22
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Ping TAN, Xu-feng LI, Jin DING, Zhi-sheng CUI, Ji-en MA, Yue-lan SUN, Bing-qiang HUANG, You-tong FANG. Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2100494 @article{title="Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection", %0 Journal Article TY - JOUR
基于Mask R-CNN和多特征聚类模型的接触网绝缘子识别和缺陷检测机构:1浙江科技学院,自动化与电气工程学院,中国杭州,310023;2浙江大学,电气工程学院,中国杭州,310027 目的:绝缘子是高速铁路接触网的重要组成部分。绝缘子的故障会导致绝缘劣化,甚至会导致接触网断电,所以绝缘子缺陷检测对高速列车运行具有重要意义。本文旨在分析巡检车拍摄的接触网绝缘子的图像特点,结合绝缘子破损、污垢、异物和闪络四类主要缺陷,研究一种智能图像处理方法,以期有效识别绝缘子及其缺陷。 创新点:1.通过MaskR-CNN模型,实现绝缘子区域像素级切割及旋正;2.提出垂直投影技术,实现绝缘子单片区域快速准确定位;3.通过多特征融合和聚类分析模型,检测绝缘子破损、污垢、异物和闪络。 方法:1.通过分析接触网图像的特点,采用Mask R-CNN方法实现绝缘子区域定位、前后景像素分割以及倾斜修正(图5);2.通过垂直投影方法,得到绝缘子各片空间坐标信息(图6);3.通过提取图像梯度、纹理和灰度特征(公式(2)~(4)),运用特征融合聚类方法(公式(5)~(7)),计算其相邻片之间的特征分布差异(公式(8));4.基于实际拍摄图片构建实验测试样本,并分析实验过程及结果,验证所提方法的可行性和有效性。 结论:1.Mask R-CNN是一种高效的目标识别和实例分割深度学习模型;它在绝缘子识别方面展现了鲁棒性和高精度。2.实验表明,本文提出的绝缘子像素区域切割和倾斜校正具有较高精度。3.对于绝缘子缺陷检测,本文提出的多特征融合聚类分析模型测试结果显示其具有较高的缺陷识别精确度。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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