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/hoiliu-0801/DQ-DETR.
@article{arxiv.2404.03507,
title = {DQ-DETR: DETR with Dynamic Query for Tiny Object Detection},
author = {Yi-Xin Huang and Hou-I Liu and Hong-Han Shuai and Wen-Huang Cheng},
journal= {arXiv preprint arXiv:2404.03507},
year = {2024}
}
Comments
Accepted by ECCV 2024. Our code will be available at https://github.com/hoiliu-0801/DQ-DETR