Towards Balanced Supervision: Cumulative Quality-Based Dynamic Assignment for Fine-Grained Remote Sensing Object Detection

Fine-grained object detection (FGOD) is crucial for identifying visually similar sub-categories in remote sensing imagery. However, existing detectors suffer from severe supervision imbalance because static label assignment strategies assign a fixed number of positive samples to all sub-categories and targets. To address this challenge, this paper presents Cumulative Quality-based Dynamic Assignment (CQDA), a fine-grained aware label assignment algorithm that dynamically calculates the optimal positive budget for each instance based on its cumulative alignment quality. Moreover, to further resolve feature-space confusion, this paper introduces two modules: a frequency-decoupled enhancement algorithm to sharpen discriminative features, and an orthogonal classification head to maximize inter-class separability. Integrated into the KFIoU framework, extensive experiments demonstrate that the proposed method consistently achieves performance improvements of 4.2, 15.8, and 35.3 in [email protected] on the fine-grained oriented object detection datasets FAIR1M-v2, MAR20, and ShipRSImageNet, respectively.