Instance-Aware Distillation for Efficient Object Detection in Remote Sensing Images

Practical applications ask for object detection models that achieve high performance at low overhead. Knowledge distillation demonstrates favorable potential in this case by transferring knowledge from a cumbersome teacher model to a lightweight student model. However, previous distillation methods are plagued with massive misleading background information in remote sensing images and ignore investigating the relationships between different instances. In this article, we propose an instance-aware distillation (InsDist for short) method to derive efficient remote sensing object detectors. Our InsDist combines feature-based and relation-based knowledge distillation to make the most of instance-related information in the knowledge transfer from the teacher to the student. On one hand, we propose a parameter-free masking module to decouple instance-related foreground from instance-irrelevant background in multiscale features. On the other hand, we construct the relationships between different instances to enhance the learning of intraclass compactness and interclass dispersion. The student comprehensively imitates both features and relationships from the teacher, yielding considerable effectiveness in dealing with complex remote sensing images. In addition, our InsDist can be easily built on mainstream object detectors with negligible extra cost. Extensive experiments on two large-scale remote sensing object detection datasets, namely DIOR and DOTA, show that our InsDist obtains noticeable gains over other distillation methods for both one-stage and two-stage, as well as both anchor-based and anchor-free detectors. The source code will be publicly available at https://github.com/swift1988/InsDist .