Open-vocabulary object detection enables models to localize and recognize objects beyond a predefined set of categories and is expected to achieve recognition capabilities comparable to human performance. In this study, we aim to evaluate the performance of existing models on open-vocabulary object detection tasks under low-quality image conditions. For this purpose, we introduce a new dataset that simulates low-quality images in the real world. In our evaluation experiment, we find that although open-vocabulary object detection models exhibited no significant decrease in mAP scores under low-level image degradation, the performance of all models dropped sharply under high-level image degradation. OWLv2 models consistently performed better across different types of degradation, while OWL-ViT, GroundingDINO, and Detic showed significant performance declines. We will release our dataset and codes to facilitate future studies.

翻译：开放词汇目标检测使模型能够定位和识别超出预定义类别集合的物体，有望实现与人类性能相当的识别能力。本研究旨在评估现有模型在低质量图像条件下执行开放词汇目标检测任务的性能。为此，我们引入了一个模拟现实世界低质量图像的新数据集。在我们的评估实验中，我们发现尽管开放词汇目标检测模型在低程度图像退化下mAP分数未出现显著下降，但在高程度图像退化下所有模型的性能均急剧下降。OWLv2模型在不同类型的退化中始终表现更优，而OWL-ViT、GroundingDINO和Detic则表现出显著的性能下降。我们将公开我们的数据集和代码，以促进未来的研究。