NEU-DET-and-GC10-DET-with-YOLO-fromat
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902261545828352 |
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| author | guan, li |
| author_facet | guan, li |
| contents | <p>The raw data of this dataset originates from NEU-DET (K. Song and Y. Yan, “A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,” Applied Surface Science, vol. 285, pp. 858-864, Nov. 2013) and GC10-DET (Lv, X.; Duan, F.; Jiang, J.-j.; Fu, X. and Gan, L. “Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network.” Sensors 2020, 20, 1562.).</p> <p>The following preprocessing steps were applied to the original data in this dataset:</p> <ol> <li> <p>Format conversion: The data formats of both NEU-DET and GC10-DET were converted into YOLO format.</p> </li> <li> <p>Noise addition: Random noise at 15 dB to 30 dB was added to the images of both NEU-DET and GC10-DET.</p> </li> <li> <p>Data splitting: The data in both NEU-DET and GC10-DET were divided into training and validation sets in an 8:2 ratio.</p> </li> </ol> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16882077 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | NEU-DET-and-GC10-DET-with-YOLO-fromat guan, li <p>The raw data of this dataset originates from NEU-DET (K. Song and Y. Yan, “A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,” Applied Surface Science, vol. 285, pp. 858-864, Nov. 2013) and GC10-DET (Lv, X.; Duan, F.; Jiang, J.-j.; Fu, X. and Gan, L. “Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network.” Sensors 2020, 20, 1562.).</p> <p>The following preprocessing steps were applied to the original data in this dataset:</p> <ol> <li> <p>Format conversion: The data formats of both NEU-DET and GC10-DET were converted into YOLO format.</p> </li> <li> <p>Noise addition: Random noise at 15 dB to 30 dB was added to the images of both NEU-DET and GC10-DET.</p> </li> <li> <p>Data splitting: The data in both NEU-DET and GC10-DET were divided into training and validation sets in an 8:2 ratio.</p> </li> </ol> |
| title | NEU-DET-and-GC10-DET-with-YOLO-fromat |
| url | https://doi.org/10.5281/zenodo.16882077 |