DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
<|reference_start|>DQ-DETR: DETR with Dynamic Query for Tiny Object Detection: Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects. Also, DETR-like methods using a fixed number of queries make them unsuitable for aerial datasets, which only contain tiny objects, and the numbers of instances are imbalanced between different images. Thus, we present a simple yet effective model, named DQ-DETR, which consists of three different components: categorical counting module, counting-guided feature enhancement, and dynamic query selection to solve the above-mentioned problems. DQ-DETR uses the prediction and density maps from the categorical counting module to dynamically adjust the number of object queries and improve the positional information of queries. Our model DQ-DETR outperforms previous CNN-based and DETR-like methods, achieving state-of-the-art mAP 30.2% on the AI-TOD-V2 dataset, which mostly consists of tiny objects. Our code will be available at https://github.com/Katie0723/DQ-DETR.<|reference_end|>
@article{huang2024dq-detr:,
    title={DQ-DETR: DETR with Dynamic Query for Tiny Object Detection},
    author={Yi-Xin Huang, Hou-I Liu, Hong-Han Shuai, Wen-Huang Cheng},
    journal={arXiv preprint arXiv:2404.03507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03507},
    primaryClass={cs.CV}
}