Aerial object detection has been a hot topic for many years due to its wide application requirements. However, most existing approaches can only handle predefined categories, which limits their applicability for the open scenarios in real-world. In this paper, we extend aerial object detection to open scenarios by exploiting the relationship between image and text, and propose OVA-DETR, a high-efficiency open-vocabulary detector for aerial images. Specifically, based on the idea of image-text alignment, we propose region-text contrastive loss to replace the category regression loss in the traditional detection framework, which breaks the category limitation. Then, we propose Bidirectional Vision-Language Fusion (Bi-VLF), which includes a dual-attention fusion encoder and a multi-level text-guided Fusion Decoder. The dual-attention fusion encoder enhances the feature extraction process in the encoder part. The multi-level text-guided Fusion Decoder is designed to improve the detection ability for small objects, which frequently appear in aerial object detection scenarios. Experimental results on three widely used benchmark datasets show that our proposed method significantly improves the mAP and recall, while enjoying faster inference speed. For instance, in zero shot detection experiments on DIOR, the proposed OVA-DETR outperforms DescReg and YOLO-World by 37.4% and 33.1%, respectively, while achieving 87 FPS inference speed, which is 7.9x faster than DescReg and 3x faster than YOLO-world. The code is available at https://github.com/GT-Wei/OVA-DETR.

本研究解决了现有空中目标检测方法仅能处理预定义类别的问题，通过引入图像与文本之间的关系，提出了高效的开放词汇检测器OVA-DETR。该方法通过区域-文本对比损失替代传统检测框架中的类别回归损失，从而打破类别限制，显著提高了小物体检测能力和推断速度，实验结果表明其在多个基准数据集上有显著提升。

OVA-DETR：基于图像-文本对齐和融合的开放词汇空中目标检测