出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In the 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.
关键词：object detection; small object detection; instance segmentation; small objects; data;
augmentation.
