Name
Title | ||
|---|---|---|
Vision-based Detection of Steel Billet Surface Defects via Fusion of Multiple Image Features. |
Abstract | ||
|---|---|---|
Automatic inspection techniqueshave been widely employed toachieve high productivitywhile ensuring high-quality productsin steelmaking industry. In this paper, a vision-based detection framework for automatically detectingdifferent types of steel billet surface defects is proposed. The defects considered in this study includescratches, corner cracks, sponge cracks, slivers, and roll marks. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-basedimage enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-basedimage feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-484-8-1239 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
defect detection,steel billet,feature fusion,discrete wavelet transform,region of interest,high dynamic range | Computer vision,Feature (computer vision),Computer science,Fusion,Vision based,Artificial intelligence | Conference |
Volume | ISSN | Citations |
274 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao-yung Hsu | 1 | 204 | 13.38 |
| Li-Wei Kang | 2 | 340 | 19.30 |
| Chih-Yang Lin | 3 | 393 | 48.15 |
| Chia-Hung Yeh | 4 | 367 | 42.15 |
| Chia-Tsung Lin | 5 | 0 | 0.68 |