IEICE TRANSACTIONS on Information
Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.2 pp.450-453
- Publication Date
- 2020/02/01
- Publicized
- 2019/11/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8178
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
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.
Keyword
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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},}
Copy
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 -
English
