Towards Practical Object Detection with Limited Data: A Feature Distillation Framework

Underwater structural surface defect detection—such as identifying cavities and spalling—faces significant challenges due to complex environments, scarce annotated data, and the reliance of modern detectors on large-scale datasets. While current approaches often combine large-data training with fine-tuning or image enhancement, they still require extensive underwater samples and are typically too computationally heavy for resource-constrained robotic platforms. To address these issues, we introduce a defect detection model based on feature distillation, which achieves high detection accuracy with limited samples. We tackle three key challenges: enhancing sample diversity under data scarcity, selecting and training a baseline model that balances accuracy and efficiency, and improving lightweight model performance using augmented samples under computational constraints. By integrating a feature distillation mechanism with a sample augmentation strategy, we develop a compact detection strategy and framework that delivers notable performance gains in limited data, offering a practical and efficient solution for real-world underwater inspection.