  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.
