Abstract: To address the critical challenge of catastrophic forgetting in deep learning models, this paper proposes a prototype contrast based incremental few-shot object detection algorithm for synthetic aperture radar (SAR) image, namely as InFSAR. Firstly, the detector is pre-trained on a base dataset to establish preliminary feature extraction capabilities. Secondly, a class prototype representation generation module is designed to construct a set of class prototypes that represent the intrinsic characteristics of the data. During the incremental learning phase, a mixed class prototype contrastive encoding module is designed to effectively learn discriminative features between new and base classes. Furthermore, to mitigate catastrophic forgetting, a prototype calibration strategy is introduced, guiding the model predictive distribution on class prototypes to gradually approximate the true distribution, thereby maintaining stability in recognizing base classes. Experimental results on the few-shot object detection dataset SRSDD-v1.0 show that under the 5-shot setting, InFSAR achieves a detection accuracy of 46.50% for fine-grained ship targets. Additionally, the proposed method can incrementally detect and identify new categories with limited annotations without requiring access to the base class training data.