CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Feature Fusion Based Insulator Detection for Aerial Inspection
YAN Tiantian1,2; YANG Guodong2; YU Junzhi2
2017
Conference NameThe 36th Chinese Control Conference
Conference Date2017-7
Conference PlaceDalian, China
Abstract
; This paper presents a detection method of insulator stings for aerial inspection based on feature-fusion. The local subimages of insulator strings are firstly collected from aerial videos and tagged to establish a training dataset. The fusion feature is then composed by the histogram of oriented gradients (HOG) feature and local binary pattern (LBP) feature after the principal component analysis (PCA) dimension reduction separately. A training model is developed by SVM algorithm with the fusion feature. At the detection phase, threshold segmentation and morphological operation are adopted to preprocess the images. The sliding window method is then used to search the candidate region and the non-maximum suppression (NMS) method is adopted to fuse the candidate windows. Finally, the position of the insulator strings can be calculated by linear fitting. Both the efficiency and the effectiveness of the proposed method are verified through experiments on locating the multi-angle insulator strings under complex backgrounds.
KeywordInsulator Location Feature-fusion Power Line Inspection Hog Lbp
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20921
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.University of Chinese Academy of Sciences
2.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation of, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
YAN Tiantian,YANG Guodong,YU Junzhi. Feature Fusion Based Insulator Detection for Aerial Inspection[C],2017.
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