电波科学学报2026，Vol.41Issue(1)：65-72,8.DOI:10.12265/j.cjors.2025150
InFSAR:基于原型对比的SAR图像增量小样本目标检测
InFSAR:prototype contrast based incremental few-shot object detection for SAR images
摘要
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 46.5%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.关键词
合成孔径雷达(SAR)图像/目标检测/小样本学习/类原型/增量学习Key words
synthetic aperture radar(SAR)images/object detection/few-shot learning/class prototypes/incremental learning分类
信息技术与安全科学引用本文复制引用
万辉耀,马克,陈杰,黄志祥,曹宜策,王帅..InFSAR:基于原型对比的SAR图像增量小样本目标检测[J].电波科学学报,2026,41(1):65-72,8.基金项目
国家自然科学基金(62471006,U23B2007) （62471006,U23B2007）
安徽省科技创新攻坚计划项目(202423h08050007) （202423h08050007）
国家自然科学基金青年项目(62401007)National Natural Science Foundation of China(62471006,U23B2007) （62401007）
Anhui Provincial Science and Technology Tackling Key Problems Project(202423h08050007) （202423h08050007）
National Science Foundation for Young Scientists of China(62401007) （62401007）
