典型文献
Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection
文献摘要：
Rod insulators are vital parts of the catenary of high speed railways (HSRs). There are many different catenary insulators, and the background of the insulator image is complicated. It is difficult to recognise insulators and detect defects automatically. In this paper, we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network (R-CNN) and an image processing model. Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator. Gradient, texture, and gray feature fusion (GTGFF) and a K-means clustering analysis model (KCAM) are proposed to detect broken insulators, dirt, foreign bodies, and flashover. Using this model, insulator recognition and defect detection can achieve a high recall rate and accuracy, and generalized defect detection. The algorithm is tested and verified on a dataset of realistic insulator images, and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.
文献关键词：
中图分类号：
作者姓名：
Ping TAN;Xu-feng LI;Jin DING;Zhi-sheng CUI;Ji-en MA;Yue-lan SUN;Bing-qiang HUANG;You-tong FANG
作者机构：
School of Automation and Electrical Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China
文献出处：
引用格式：
[1]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].浙江大学学报（英文版）（A辑：应用物理和工程）,2022(09):745-756
A类：
multifeature,HSRs,GTGFF,KCAM
B类：
Mask,clustering,model,catenary,recognition,detection,Rod,insulators,are,vital,parts,high,speed,railways,There,many,different,background,complicated,It,difficult,recognise,defects,automatically,In,this,paper,we,intelligent,algorithm,region,convolutional,neural,network,processing,Vertical,projection,technology,used,achieve,single,shed,positioning,precise,cutting,Gradient,texture,gray,fusion,means,analysis,proposed,broken,dirt,foreign,bodies,flashover,Using,can,recall,rate,accuracy,generalized,tested,verified,dataset,realistic,images,reliability,satisfy,current,requirements,inspection,maintenance
AB值：
0.504359
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