Negative-Core Sample Knowledge Distillation for Oriented Object Detection in Remote Sensing Image

Knowledge distillation (KD) has been one of the most effective methods for enhancing the performance of lightweight detectors, crucial for remote sensing edge intelligence models. However, many mainstream distillation methods that are centered around the paradigm of distilling positive samples show weak exploitation of the student's potential. This arises due to these methods overlooking the core teacher-student difference in remote sensing scenarios with vast and object-similar backgrounds. In this article, from the point of distillation sample and knowledge hierarchy, we design a negative-core sample knowledge distillation (NSD) method for improving the performance of the lightweight object detection model. Specifically, a negative-core sample (NCS) is innovatively employed to transfer effective background discrimination knowledge for bridging the core difference. KD for NCS across four levels-pixel, logit, box, and angle-are customized to fully leverage the teacher's insights. Category direction estimation (CE) is incorporated into the angle KD to convey NCS-oriented knowledge more effectively. Extensive experiments conducted on multiple remote sensing datasets achieve state-of-the-art (SOTA) performance, demonstrating the effectiveness of the proposed NSD. Codes are available at https://github.com/Changan00/NSD.