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

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.

Corrosion Damage Detection in Headrace Tunnel Using YOLOv7 with Continuous Wall Images

“…There have been many other studies on deformation detection in concrete [10][11][12][13][14], but relatively few studies have been performed on steel damage. Nonetheless, there has been research [15][16][17][18] on the detection of corrosion points in steel using fully convolutional networks [19], including the study of Shi et al As mentioned above, various methods have been discussed and actively researched for deterioration/damage detection in bridges. However, for headrace tunnels, which were the focus of the present study, effective maintenance and management methods have not been developed, despite their importance, and little research has been performed on the subject [20].…”

“…The total amount of data for each type of label used as training and validation data is presented in Table 1. No previous studies have involved the detection of chalked areas in headrace tunnels; however, in previous studies involving the detection of cracks in concrete structures, the number of data used for learning and validation ranged from several hundred to several tens of thousands [11,12,14,15]. Moreover, a wide variety of detection methods were employed, such as CNNs, encoder-decoder networks, segmentation, and object detection [7,8,11].…”