On the Importance of Large Objects in CNN Based Object Detection Algorithms

Ahmed Ben Saad, Gabriele Facciolo, Axel Davy; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 533-542

Object detection models, a prominent class of machine learning algorithms, aim to identify and precisely locate objects in images or videos. However, the task of accurately localizing objects within images yields uneven performances sometimes caused by the objects sizes and the quality of the images and labels. 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 address this by introducing a weighting term into the loss during training. This term is a function of the object area size. We show that giving more weight to large objects leads to improvement in detection scores across all sizes and so an overall improvement in Object Detectors performances (+2% mAP on small objects, +2% on medium and +4% on large on COCO val 2017 with InternImage-T). Additional experiments and ablation studies with different models and on different dataset further confirm the robustness of our findings.