Name
Abstract | ||
|---|---|---|
AbstractImaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation REMPS technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1111/mice.12098 | Periodicals |
Keywords | Field | DocType |
image processing,civil engineering,support vector machines,inspection | Computer vision,Color space,Segmentation,Support vector machine,Image processing,Filter (signal processing),Sobel operator,Artificial intelligence,Pixel,Engineering,Cluster analysis | Journal |
Volume | Issue | ISSN |
29 | 9 | 1093-9687 |
Citations | PageRank | References |
8 | 0.72 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael O'Byrne | 1 | 21 | 1.99 |
| Bidisha Ghosh | 2 | 85 | 9.13 |
| Franck Schoefs | 3 | 51 | 6.76 |
| Vikram Pakrashi | 4 | 56 | 7.61 |