Evaluating the Performance of Open-Vocabulary Object Detection in Low-quality Image

• URL: http://arxiv.org/abs/2512.22801v2
• Date: Fri, 02 Jan 2026 12:29:39 GMT
• Title: Evaluating the Performance of Open-Vocabulary Object Detection in Low-quality Image
• Authors: Po-Chih Wu,
• Abstract summary: We introduce a new dataset that simulates low-quality images in the real world.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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