On the Importance of Large Objects in CNN Based Object Detection
  Algorithms
Ahmed Ben Saad Gabriele Facciolo Axel Davy
Object detection models, a prominent class of machine learning algorithms,
aim to identify and precisely locate objects in images or videos. However, this
task might yield uneven performances sometimes caused by the objects sizes and
the quality of the images and labels used for training. In this paper, we
highlight the importance of large objects in learning features that are
critical for all sizes. Given these findings, we propose to introduce a
weighting term into the training loss. This term is a function of the object
area size. We show that giving more weight to large objects leads to improved
detection scores across all object sizes and so an overall improvement in
Object Detectors performances (+2 p.p. of mAP on small objects, +2 p.p. on
medium and +4 p.p. on large on COCO val 2017 with InternImage-T). Additional
experiments and ablation studies with different models and on a different
dataset further confirm the robustness of our findings.