Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/517138
Title: Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images
Authors: Zhenbing Zhao;Xiaoqing Fan;Guozhi Xu;Lei Zhang;Yincheng Qi;Ke Zhang
Year: 2017
Publisher: IEEE
Abstract: Insulator detection using an infrared image is challenged by variance of temperature, orientations, and a cluttered background. A robust and discriminative representation of insulators in electric power systems is needed. This paper proposes a novel method for generating this type of representation in infrared images by taking advantage of high-level discriminative Convolutional Neural Networks (CNNs) to feature the extraction framework and the deformation invariant nature of the Vector of Locally Aggregated Descriptors (VLAD) aggregator. Different from existing methods, we delve deep into the convolutional feature maps. We first extract deep activation maps from convolutional layers of a pretrained deep model and replace the last three fully-connected layers with a VLAD pooling layer to generate the representation of an insulator. Then, we train a Support Vector Machine (SVM) for binary classification. To further verify the effectiveness and robustness of our proposed feature, an insulator detection pipeline based on an object proposal is introduced. The experimental results show that our proposed method can achieve an accuracy of 93&x0025;. Meanwhile, the detection results demonstrate that our insulator detection pipeline has satisfied performance goals.
Description: 
URI: http://dl.kums.ac.ir/handle/Hannan/517138
volume: 5
More Information: 21831,
21839
Appears in Collections:2017

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Title: Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images
Authors: Zhenbing Zhao;Xiaoqing Fan;Guozhi Xu;Lei Zhang;Yincheng Qi;Ke Zhang
Year: 2017
Publisher: IEEE
Abstract: Insulator detection using an infrared image is challenged by variance of temperature, orientations, and a cluttered background. A robust and discriminative representation of insulators in electric power systems is needed. This paper proposes a novel method for generating this type of representation in infrared images by taking advantage of high-level discriminative Convolutional Neural Networks (CNNs) to feature the extraction framework and the deformation invariant nature of the Vector of Locally Aggregated Descriptors (VLAD) aggregator. Different from existing methods, we delve deep into the convolutional feature maps. We first extract deep activation maps from convolutional layers of a pretrained deep model and replace the last three fully-connected layers with a VLAD pooling layer to generate the representation of an insulator. Then, we train a Support Vector Machine (SVM) for binary classification. To further verify the effectiveness and robustness of our proposed feature, an insulator detection pipeline based on an object proposal is introduced. The experimental results show that our proposed method can achieve an accuracy of 93&x0025;. Meanwhile, the detection results demonstrate that our insulator detection pipeline has satisfied performance goals.
Description: 
URI: http://dl.kums.ac.ir/handle/Hannan/517138
volume: 5
More Information: 21831,
21839
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
8052216.pdf1.57 MBAdobe PDFThumbnail
Preview File
Title: Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images
Authors: Zhenbing Zhao;Xiaoqing Fan;Guozhi Xu;Lei Zhang;Yincheng Qi;Ke Zhang
Year: 2017
Publisher: IEEE
Abstract: Insulator detection using an infrared image is challenged by variance of temperature, orientations, and a cluttered background. A robust and discriminative representation of insulators in electric power systems is needed. This paper proposes a novel method for generating this type of representation in infrared images by taking advantage of high-level discriminative Convolutional Neural Networks (CNNs) to feature the extraction framework and the deformation invariant nature of the Vector of Locally Aggregated Descriptors (VLAD) aggregator. Different from existing methods, we delve deep into the convolutional feature maps. We first extract deep activation maps from convolutional layers of a pretrained deep model and replace the last three fully-connected layers with a VLAD pooling layer to generate the representation of an insulator. Then, we train a Support Vector Machine (SVM) for binary classification. To further verify the effectiveness and robustness of our proposed feature, an insulator detection pipeline based on an object proposal is introduced. The experimental results show that our proposed method can achieve an accuracy of 93&x0025;. Meanwhile, the detection results demonstrate that our insulator detection pipeline has satisfied performance goals.
Description: 
URI: http://dl.kums.ac.ir/handle/Hannan/517138
volume: 5
More Information: 21831,
21839
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
8052216.pdf1.57 MBAdobe PDFThumbnail
Preview File