论文标题
平均精度对边界盒扰动的敏感性
Sensitivity of Average Precision to Bounding Box Perturbations
论文作者
论文摘要
对象检测是一项基本视觉任务。它在学术界进行了高度研究，并在行业中广泛采用。平均精度（AP）是评估对象检测器的标准分数。但是，我们对该分数的微妙之处的理解是有限的。在这里，我们量化了AP对边界框扰动的敏感性，并表明AP对小型翻译非常敏感。只有一个像素移位足以将模型的地图降低8.4％。仅一个像素偏移的小物体上的地图掉落为23.1％。当将地面真相（GT）框用作预测时的相应数字分别为23％和41.7％。这些结果解释了为什么随着模型变得更好，为什么实现更高的地图变得越来越困难。我们还研究了盒子缩放对AP的影响。代码和数据可从https://github.com/aliborji/ap_box_perturbation获得。
Object detection is a fundamental vision task. It has been highly researched in academia and has been widely adopted in industry. Average Precision (AP) is the standard score for evaluating object detectors. Our understanding of the subtleties of this score, however, is limited. Here, we quantify the sensitivity of AP to bounding box perturbations and show that AP is very sensitive to small translations. Only one pixel shift is enough to drop the mAP of a model by 8.4%. The mAP drop over small objects with only one pixel shift is 23.1%. The corresponding numbers when ground-truth (GT) boxes are used as predictions are 23% and 41.7%, respectively. These results explain why achieving higher mAP becomes increasingly harder as models get better. We also investigate the effect of box scaling on AP. Code and data is available at https://github.com/aliborji/AP_Box_Perturbation.
