A minority-friendly learning method for object detection: joint optimization of oversampling-driven data balancing and loss reconstruction

Abstract

Class imbalance in object detection severely degrades the recognition of minority categories, leading to unstable training and unsatisfactory detection accuracy for rare objects. Existing methods usually address this issue either at the data level or at the loss level alone, while lacking effective coordination between the two. To this end, this paper proposes a minority-friendly learning method for object detection through the joint optimization of oversampling-driven data rebalancing and loss reconstruction. Specifically, a category-aware oversampling strategy is introduced to alleviate sample distribution bias, and a minority-oriented loss reformulation mechanism is designed to strengthen the contribution of underrepresented classes during training. The proposed method introduces little additional model complexity and can be integrated into the training pipeline without modifying the detector backbone or detection head. Experiments on an imbalanced MS COCO 2017 setting demonstrate consistent gains in mAP, Recall, and minority-class AP, while maintaining stable performance on majority classes.