Reducing false positives is essential for enhancing object detector performance, as reflected in the mean Average Precision (mAP) metric. Although object detectors have achieved notable improvements and high mAP scores on the COCO dataset, analysis reveals limited progress in addressing false positives caused by non-target visual clutter-background objects not included in the annotated categories. This issue is particularly critical in real-world applications, such as fire and smoke detection, where minimizing false alarms is crucial. In this study, we introduce COCO-FP, a new evaluation dataset derived from the ImageNet-1K dataset, designed to address this issue. By extending the original COCO validation dataset, COCO-FP specifically assesses object detectors' performance in mitigating background false positives. Our evaluation of both standard and advanced object detectors shows a significant number of false positives in both closed-set and open-set scenarios. For example, the AP50 metric for YOLOv9-E decreases from 72.8 to 65.7 when shifting from COCO to COCO-FP. The dataset is available at https://github.com/COCO-FP/COCO-FP.

翻译：降低误报对于提升目标检测器性能至关重要，这反映在平均精度均值（mAP）指标中。尽管目标检测器在COCO数据集上取得了显著改进和高mAP分数，但分析显示，在解决由非目标视觉杂波——即标注类别中未包含的背景物体——引起的误报方面进展有限。该问题在现实应用中尤为关键，例如火灾烟雾检测，其中最小化误报至关重要。在本研究中，我们引入了COCO-FP，这是一个源自ImageNet-1K数据集的新评估数据集，旨在解决此问题。通过扩展原始COCO验证数据集，COCO-FP专门评估目标检测器在减少背景误报方面的性能。我们对标准和先进目标检测器的评估显示，在闭集和开集场景下均存在大量误报。例如，当从COCO切换到COCO-FP时，YOLOv9-E的AP50指标从72.8降至65.7。该数据集可通过https://github.com/COCO-FP/COCO-FP获取。