A Framework for Imbalanced SAR Ship Classification: Curriculum Learning Weighted Loss Functions and a Novel Evaluation Metric

Ch Muhammad Awais, Marco Reggiannini, Davide Moroni; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1570-1578

Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset facilitating the accurate classification of its nine classes. Our method achieved notable improvements including a 6.53% average increase in F1-scores compared to baseline models and successfully identified all classes including previously misclassified ones. To better evaluate model performance on long-tailed datasets we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset our approach and metric are broadly applicable to other imbalanced classification problems.