Low-light Object Detection
<|reference_start|>Low-light Object Detection: 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.<|reference_end|>
@article{li2024low-light,
    title={Low-light Object Detection},
    author={Pengpeng Li, Haowei Gu, Yang Yang},
    journal={arXiv preprint arXiv:2405.03519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03519},
    primaryClass={cs.CV}
}