Abstract：Deep neural network training needs a large number of sample data, but for infrared ship targets, the sample size of infrared ship targets with different types and perspectives is small and difficult to collect, which makes it very difficult for deep learning training. In order to reduce the demand for real infrared ship target data in deep learning, a large number of simulated infrared ship images and a small number of real infrared ship images are used as samples for training. In order to solve the problem of cross domain adaptability between simulated infrared ship image and real infrared ship image, the feature adaptive method from coarse to fine is used to realize the cross domain target detection function. Experimental results show that the proposed algorithm has high detection accuracy for real infrared ship targets.
王悦行, 吴永国, 徐传刚. 基于深度迁移学习的红外舰船目标检测算法[J]. 空天防御, 2021, 4(4): 61-66.
WANG Yuexing, WU Yongguo, XU Chuangang. Infrared Ship Target Detection Algorithm Based on Deep Transfer Learning. Air & Space Defense, 2021, 4(4): 61-66.
