摘要
目的:在图像缺陷样本少和一致性差的情况下,实现精确可靠的接触网绝缘子缺陷检测。创新点:提山一种基于瓷片轮廓特征及灰度相似度匹配的融合算法,实现了绝缘子瓷片的轮廓提取及绝缘子各瓷片的精准分离,并构建了基于瓷片间距和灰度相似度匹配的绝缘子缺陷检测模型。方法:1.采用同一个绝缘子相邻瓷片两两比较的方法进行缺陷检测,解决图像缺陷样本少和一致性差的问题。2.分两步进行检测(Fig.2):(1)基于水平梯度特征提取绝缘子各瓷片轮廓,并对瓷片轮廓内像素进行复原;(2)计算瓷片间距和灰度相似度,并与设置的阈值进行比较以区分正常绝缘子和缺陷绝缘子。结论:1.实验表明,基于轮廓特征及灰度相似度匹配的方法能够有效区分正常绝缘子和缺陷绝缘子。2.在图片数据集中,测试达到了99.50%的高召回率和91.71%的高精确度,满足了目前较高水平的接触网绝缘子缺陷检测的要求。
Insulators are the key components of high speed railway catenaries. Insulator failures can cause outages and affect the safe operation of high speed railways. It is important to perform insulator defect detection. Due to the collection of insulator images by moving catenary inspection vehicles, the consistency of the images is poor, and the number of insulator defect samples is very small. An algorithm of deep learning and conventional template matching cannot meet the requirements of insulator defect detection. This paper proposes a fusion algorithm based on the shed contour features and gray similarity matching. High accuracy and consistency of contour extraction and precise separation of each insulator shed were realized. An insulator defect detection model based on the spacing distance of the sheds and the gray similarity was constructed. Experiments show that the method based on the contour features and gray similarity matching can effectively classify normal insulators and defective insulators. Recall of 99.50% and high precision of 91.71% were achieved in the test of the image data set, and this can meet the requirements for the reliability and high precision of a detection algorithm for catenary insulators.
基金
Project supported by the National Natural Science Foundation of China(Nos.51677171,51637009,51577166,and 51827810)
the Zhejiang Provincial Natural Science Foundation of China(No.LY17C100001)
the National Key R&D Program of China(No.2018YFB0606000)
the China Scholarship Council(No.201708330502)
关键词
高铁绝缘子
缺陷检测
轮廓提取
瓷片分离
灰度相似度
High speed railway insulator
Defect detection
Contour extraction
Shed separation
Gray similarity
