Distilling Missed Samples for Remote Sensing Oriented Object Detectors

Knowledge distillation is one of the most effective methods for improving the performance of lightweight detectors, which is crucial for the development of remote sensing (RS) edge intelligence. However, lightweight detectors exhibit significant missed detection when detecting RS targets with arbitrary orientations and extreme scales. This is due to insufficient discriminative features learned by the lightweight student network under standard training, as well as the lack of a targeted, coordinated sample-optimization design. To address these problems, this paper proposes a knowledge Distillation method based on Missed-sample Reinforcement, named DMR. Specifically, a Cross-scale Missed-sample Enhancement module is designed to transfer the teacher's classification knowledge, thereby bridging the core differences in classification. Based on differences between teacher and student predictions, it dynamically selects missed-detection samples, guiding the student model to focus on them. A Dynamic Local Feature Alignment module is also proposed to distill the local key-feature distribution from the teacher model. It efficiently acquires contextual information from local features, guiding the student model to extract discriminative target features. Furthermore, we introduce a generative adversarial process to constrain the student's angle predictions, enabling it to fully utilize the angular information predicted by the teacher model. Extensive experiments on three public RS datasets achieve state-of-the-art (SOTA) performance, demonstrating the effectiveness of the proposed method.