The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste trabalho, apresentamos um novo método de detecção de ferrugem baseado na classificação de uma classe e representação esparsa L2 (SR) com fusão de decisão. Primeiramente, é proposto um novo descritor de contraste de cores para extrair as características de ferrugem de imagens de estruturas de aço. Considerando que os padrões de recursos de ferrugem são mais simplificados do que aqueles de não ferrugem, o classificador de máquina de vetores de suporte (SVM) de classe única e o classificador L2 SR são projetados com esses recursos de imagem de ferrugem, respectivamente. Depois disso, uma regra de fusão multiplicativa é defendida para combinar os módulos SVM e L2 SR de classe única, obtendo assim resultados de detecção de ferrugem mais precisos. Nos experimentos, realizamos vários experimentos e, quando comparado com outros detectores de ferrugem desenvolvidos, o método apresentado pode oferecer melhores desempenhos de detecção de ferrugem.
Guizhong ZHANG
Shandong University
Baoxian WANG
Shijiazhuang Tiedao University,Key Laboratory for Health Monitoring and Control of Large Structures of Hebei Province
Zhaobo YAN
Shijiazhuang Tiedao University
Yiqiang LI
Shijiazhuang Tiedao University
Huaizhi YANG
Beijing-Shanghai High-Speed Railway Co., Ltd.
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Guizhong ZHANG, Baoxian WANG, Zhaobo YAN, Yiqiang LI, Huaizhi YANG, "Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 2, pp. 450-453, February 2020, doi: 10.1587/transinf.2019EDL8178.
Abstract: In this work, we present one novel rust detection method based upon one-class classification and L2 sparse representation (SR) with decision fusion. Firstly, a new color contrast descriptor is proposed for extracting the rust features of steel structure images. Considering that the patterns of rust features are more simplified than those of non-rust ones, one-class support vector machine (SVM) classifier and L2 SR classifier are designed with these rust image features, respectively. After that, a multiplicative fusion rule is advocated for combining the one-class SVM and L2 SR modules, thereby achieving more accurate rust detecting results. In the experiments, we conduct numerous experiments, and when compared with other developed rust detectors, the presented method can offer better rust detecting performances.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8178/_p
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@ARTICLE{e103-d_2_450,
author={Guizhong ZHANG, Baoxian WANG, Zhaobo YAN, Yiqiang LI, Huaizhi YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion},
year={2020},
volume={E103-D},
number={2},
pages={450-453},
abstract={In this work, we present one novel rust detection method based upon one-class classification and L2 sparse representation (SR) with decision fusion. Firstly, a new color contrast descriptor is proposed for extracting the rust features of steel structure images. Considering that the patterns of rust features are more simplified than those of non-rust ones, one-class support vector machine (SVM) classifier and L2 SR classifier are designed with these rust image features, respectively. After that, a multiplicative fusion rule is advocated for combining the one-class SVM and L2 SR modules, thereby achieving more accurate rust detecting results. In the experiments, we conduct numerous experiments, and when compared with other developed rust detectors, the presented method can offer better rust detecting performances.},
keywords={},
doi={10.1587/transinf.2019EDL8178},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion
T2 - IEICE TRANSACTIONS on Information
SP - 450
EP - 453
AU - Guizhong ZHANG
AU - Baoxian WANG
AU - Zhaobo YAN
AU - Yiqiang LI
AU - Huaizhi YANG
PY - 2020
DO - 10.1587/transinf.2019EDL8178
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - In this work, we present one novel rust detection method based upon one-class classification and L2 sparse representation (SR) with decision fusion. Firstly, a new color contrast descriptor is proposed for extracting the rust features of steel structure images. Considering that the patterns of rust features are more simplified than those of non-rust ones, one-class support vector machine (SVM) classifier and L2 SR classifier are designed with these rust image features, respectively. After that, a multiplicative fusion rule is advocated for combining the one-class SVM and L2 SR modules, thereby achieving more accurate rust detecting results. In the experiments, we conduct numerous experiments, and when compared with other developed rust detectors, the presented method can offer better rust detecting performances.
ER -