Object detection is an important field in computer vision. Nevertheless, a research area that has so far not received much attention is the study into the effectiveness of anchor matching strategy and imbalance in anchor-based object detection, in particular small object detection. It is clear that the objects with larger sizes tend to match more anchors than smaller ones. This matching imbalance may result in poor performance in detecting small objects. It can be alleviated by paying more attention to the objects that match with fewer anchors. We propose an innovative flexible loss function for object detection, which is compatible with popular anchor-based detection methods. The proposed method, called the scale-balanced loss, does not add any extra computational cost to the original pipelines. By re-weighting strategy, the proposed method significantly improves the accuracy of multi-scale object detection, especially for small objects. Comprehensive experiments indicate that the scale-balanced loss achieved excellent generalization performance when separately applied to some popular detection methods. The scale-balanced loss attained up to 15% improvements on recall rates of small and medium objects in both the PASCAL VOC and MS COCO dataset. It is also beneficial to the AP result on MS COCO with an improvement of more than 1.5%.

目标检测是计算机视觉中的重要领域。然而，到目前为止，尚未引起广泛关注的研究领域是对锚点匹配策略的有效性和基于锚点的对象检测（尤其是小对象检测）中的不平衡性的研究。很明显，与较小的对象相比，较大的对象往往会匹配更多的锚。这种匹配的不平衡可能导致检测小物体时性能较差。可以通过更多地关注与较少锚点匹配的对象来缓解这种情况。我们提出了一种创新的用于物体检测的灵活损失函数，该函数与流行的基于锚的检测方法兼容。所提出的方法称为比例平衡损失，不会为原始管道增加任何额外的计算成本。通过重新加权策略，所提出的方法大大提高了多尺度目标检测的准确性，特别是对于小目标。综合实验表明，当分别应用于一些流行的检测方法时，比例平衡损失实现了出色的泛化性能。在PASCAL VOC和MS COCO数据集中，规模平衡的损失将中小型对象的召回率提高了15％。这也有利于MS COCO的AP结果提高1.5％以上。在PASCAL VOC和MS COCO数据集中，规模平衡的损失将中小型对象的召回率提高了15％。这也有利于MS COCO的AP结果提高1.5％以上。在PASCAL VOC和MS COCO数据集中，规模平衡的损失将中小型对象的召回率提高了15％。这也有利于MS COCO的AP结果提高1.5％以上。