Distribution-balanced augmentation for rough data driven object detection

Recent advancements in deep learning have highlighted the pivotal role of data in training deep neural networks. However, the persistent issue of imbalanced data distribution poses a challenge for achieving optimal performance. Existing approaches like re-sampling, re-weighting, decoupling representation and data augmentation have sought to address data imbalance by increasing data quantity and diversity, improving model performance and generalization. Notably, Copy-Paste data augmentation has shown promise but comes with significant time and computing requirements. To overcome these limitations, we propose an innovative solution called balanced object paste (BOP). BOP enhances data distribution by pasting additional objects onto target images using a set of well-defined principles, synthesizing new images and yielding promising results. The position for pasting is generated through a region generation method based on existing object distribution. Additional objects are sampled in a class-balanced manner and scaled to achieve balance across categories and sizes. BOP outperforms existing methods like Simple Copy-Paste, showcasing notable improvements of 4.1 in AP for PASCAL VOC and a speed boost of 13 times. Moreover, BOP consistently enhances detector performance across various datasets and conditions, and its versatility extends to box-labeled datasets, establishing it as a valuable tool for object detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.