ReFocal: Addressing Learning Imbalances for Accurate Tiny Object Detection in Aerial Imagery

Abstract

Tiny objects in aerial imagery usually exhibit an extremely limited number of pixels, significantly affecting the object detection model’s learning process. While existing research has attempted to improve tiny objects’ positive sample quantity for scale-balanced learning, the primary focus lies on the object level. We argue that mitigating learning imbalance requires a comprehensive consideration encompassing object-level, sample-level, and feature-level improvements. To this end, we propose ReFocal, a learning strategy comprised of ReFocal Loss and ReFocal feature pyramid network (FPN), to mitigate imbalances across these three levels. ReFocal Loss utilizes a magnitude factor to regulate the learning magnitude of objects with varying sample counts and a novel focal rate adjuster to differentiate sample quality at the sample level, enabling the detector to prioritize high-quality samples within each object. ReFocal FPN employs a refocusing mechanism to dynamically enhance detailed information in high-level feature maps without introducing additional computational cost, thus addressing the feature-level imbalance. Extensive experiments on AI-TOD-v2 and TinyPerson datasets demonstrate the superiority of our proposed method over previous single-stage methods, particularly for very tiny objects.