Class-Incremental SAR Obeject Detection Via Adaptive Distributed Response Distillation

In the task of class-incremental learning in SAR object detection, traditional deep learning training methods exhibit numerous limitations. However, updating the original detection model with only new data leads to the problem of catastrophic forgetting. To address these challenges, this paper proposes an incremental detection method based on adaptive distributed response distillation. This method effectively preserves the model’s localization and classification knowledge for old classes through the utilization of a response discreteness-based selection strategy and smooth mask-weighting mechanism. Experimental results on the MSAR dataset demonstrate that our approach achieves a 2.7% increase in average precision(AP) compared to the state-of-the-art method ERD. The proposed method can effectively mitigate catastrophic forgetting and provide higher detection accuracy.