Augmentation for small object detection
This addresses the problem of poor small object detection in computer vision, which is crucial for applications like surveillance and autonomous driving, but it is incremental as it builds on existing augmentation techniques.
The paper tackled the performance gap in detecting small versus large objects by analyzing Mask-RCNN on MS COCO, showing low overlap for small objects due to insufficient data, and proposed oversampling and copy-paste augmentation, achieving a 9.7% relative improvement in instance segmentation and 7.1% in object detection for small objects.
In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap between small ground-truth objects and the predicted anchors is much lower than the expected IoU threshold. We conjecture this is due to two factors; (1) only a few images are containing small objects, and (2) small objects do not appear enough even within each image containing them. We thus propose to oversample those images with small objects and augment each of those images by copy-pasting small objects many times. It allows us to trade off the quality of the detector on large objects with that on small objects. We evaluate different pasting augmentation strategies, and ultimately, we achieve 9.7\% relative improvement on the instance segmentation and 7.1\% on the object detection of small objects, compared to the current state of the art method on MS COCO.