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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.03519 |
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| _version_ | 1866914785798389760 |
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| author | Li, Pengpeng Gu, Haowei Yang, Yang |
| author_facet | Li, Pengpeng Gu, Haowei Yang, Yang |
| contents | In this competition we employed a model fusion approach to achieve object detection results close to those of real images. Our method is based on the CO-DETR model, which was trained on two sets of data: one containing images under dark conditions and another containing images enhanced with low-light conditions. We used various enhancement techniques on the test data to generate multiple sets of prediction results. Finally, we applied a clustering aggregation method guided by IoU thresholds to select the optimal results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-light Object Detection Li, Pengpeng Gu, Haowei Yang, Yang Computer Vision and Pattern Recognition In this competition we employed a model fusion approach to achieve object detection results close to those of real images. Our method is based on the CO-DETR model, which was trained on two sets of data: one containing images under dark conditions and another containing images enhanced with low-light conditions. We used various enhancement techniques on the test data to generate multiple sets of prediction results. Finally, we applied a clustering aggregation method guided by IoU thresholds to select the optimal results. |
| title | Low-light Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.03519 |