Title
Computer Vision-Based Detection And State Recognition For Disconnecting Switch In Substation Automation
Abstract
State recognition in disconnecting switches is important during substation automation. Here, an effective computer vision-based automatic detection and state recognition method for disconnecting switches is proposed. Taking advantage of some important prior knowledge about a disconnecting switch, the method is designed using two important features of the fixed-contact facet of such disconnecting switches. First, the Histograms of Oriented Gradients (HOG) of the fixed-contact are used to design a Linear Discriminant Analysis (LDA) target detector to position the disconnecting switches and distinguish their loci against a usual cluttered background. Then a discriminative Norm Gradient Field (NGF) feature is used to train the Support Vector Machine (SVM) state classifier to discriminate disconnecting switch states. Finally, experimental results, compared with other methods, demonstrate that the proposed method is effective and achieves a low miss rate while delivering high performance in both precision and recall rate. In addition, the adopted approach is efficient and has the potential to work in practical substation automation scenarios.
Year
DOI
Venue
2017
10.2316/Journal.206.2017.1.206-4624
INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION
Keywords
Field
DocType
Computer vision, substation automation, disconnecting switch, state recognition, histograms of oriented gradients, norm gradient field
State recognition,Control engineering,Automation,Engineering
Journal
Volume
Issue
ISSN
32
1
0826-8185
Citations 
PageRank 
References 
1
0.39
3
Authors
4
Name
Order
Citations
PageRank
Hongkai Chen132.45
Xiaoguang Zhao25418.68
Min Tan32342201.12
Shiying Sun452.48