Value-Guided Adaptive Data Augmentation for Imbalanced Small Object Detection

Abstract

Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects but under-augmentation for rare and learnable objects. To this end, we propose a value-guided adaptive data augmentation for scale- and proportion-imbalanced small object detection (ValCopy-Paste). Specifically, we first develop a non-learning object value criteria to determine whether one object should be expanded. Both scale-based learnability and quantity-based necessity are involved in this criteria. Then, the value distribution of objects in the dataset can be further constructed on the basis of the relevant object values. This helps to ensure that those uncommon, learnable objects that deserve enhancement are more likely to be enhanced. Additionally, we propose to enhance the data by pasting the sampled objects into relatively smooth portions of fresh background images, rather than arbitrary areas of any background images. This helps to boost data diversity while reducing the interference from complicated backgrounds. Evidently, our method does not require sophisticated training and just depends on the size and distribution of the objects in the dataset. Extensive experiments on MS COCO 2017 and PASCAL VOC 2012 demonstrate that our method achieves better performance than state-of-the-art methods.