Class-Incremental Learning for SAR Muti-Class Target Detection

Deep learning methods have been widely used in the field of SAR target detection, but conventional deep neural networks do not have the ability to preserve old knowledge. With new classes of SAR images constantly being acquired, typically we re-train the network using all the data, which puts a burden on storage and computing. However, if the model updates using only new data, the detector will catastrophically forget previous knowledge. To address this problem, this paper proposes a Class-incremental learning (CIL) method for SAR multi-class target detection, based on an overall framework of knowledge distillation (KD), which enables to maintain the ability of the detector to detect old classes of targets whilst learning new knowledge. The method includes two core components: First, we introduce the Localization Distillation (LD) into the incremental learning framework, preventing the impact of multi-scale SAR target introduction on small target detection capability during the incremental learning process; Second, we apply the global relationship module (GR) to the feature-review process, which strengthens the learning of target-background correlation. We performed comparison experiments on the MSAR-1.0 SAR multi-class target detection dataset, and our method has better performance compared with several mainstream incremental detection methods.