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

Ahmed Ben Saad (1, 2) , Gabriele Facciolo (1) , Axel Davy (1)

1 CB - CB - Centre Borelli - UMR 9010
2 Schlumberger AI Lab

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Ahmed Ben Saad, Gabriele Facciolo, Axel Davy. On the Importance of Large Objects in CNN Based Object Detection Algorithms. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2024, WAIKOLOA, HAWAII, United States. ⟨hal-04290988⟩