A Class-Incremental Object Detection Method for Remote Sensing Images Based on Dynamic Multiprototype Matching

In object detection task, incremental learning method enables the previously trained model better adapt to the new task using either a small amount of old data or none at all. In the incremental training processes of complex remote sensing scenes, the newly arrived data only includes new classes annotations. These new classes may exhibit spatial overlap and shape similarity with old classes or may have been labeled as background in earlier tasks, leading to a unique challenge called class semantic confusion. To address this issue, this article dynamically generate multiple representative prototypes of the different categories for refined matching the objects. To improve the matching accuracy, prototype contrastive learning is employed for expanding the distance between dissimilar prototypes and reducing the distance between similar prototypes. Meanwhile, a category perception enhancement module is proposed to enhance the aware of old categories to mitigate catastrophic forgetting. Comprehensive experimental results demonstrate that our proposed method outperforms the current state-of-the-art class-incremental object detection methods in most experimental settings on DIOR and FAIR1M datasets.