Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

Guizhong ZHANG  Baoxian WANG  Zhaobo YAN  Yiqiang LI  Huaizhi YANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.2   pp.450-453
Publication Date: 2020/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8178
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
Keyword: 
rust detection,  color contrast descriptor,  one-class classification,  L2 sparse representation,  decision fusion,  

Full Text: PDF(376.2KB)>>
Buy this Article




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.