Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.

翻译：目标检测是一项对图像或视频中物体进行位置识别和标签分类的任务。通过该过程获取的信息在计算机视觉领域的各类任务中发挥着关键作用。在目标检测中，用于训练和验证的数据通常源于公开数据集，这些数据集中每个类别在图像中的物体数量分布较为均衡。然而在实际场景中，处理类别分布极不平衡的数据集（即每个类别的物体数量差异悬殊）更为常见，这种不平衡可能会降低目标检测模型对未见测试图像的预测性能。因此，本研究提出一种方法，通过均衡训练和验证数据中各类别的分布来解决目标检测中的类别不平衡问题。我们的方法旨在通过多标签分层实现均匀的类别分布。我们不仅在通常呈现均衡类别分布的公开数据集上测试了提出方法，还在可能存在不平衡类别分布的自定义数据集上进行了验证。研究发现，我们的方法在高度不平衡且数据量较少的数据集上效果更为显著。结果表明，该方法可有效应用于类别分布严重不平衡的数据集。