Hongkai Chen, Xiaoguang Zhao, Min Tan, and Shiying Sun
Computer vision, substation automation, disconnecting switch, staterecognition, histograms of oriented gradients, norm gradient field
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 dis- connecting switches. First, the Histograms of Oriented Gradients (HOG) of the fixed-contact are used to design a Linear Discrimi- nant Analysis (LDA) target detector to position the disconnecting switches and distinguish their loci against a usual cluttered back- ground. 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 re- sults, 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.
Important Links:
Go Back
