基于迁移学习与运动特征融合的机型分类识别方法OA

To address the issue of low aircraft classification accuracy caused by the scarcity of high-precision labeled data,which hinders effective model training,an aircraft classification and recognition method based on transfer learning and motion feature fusion is proposed.Firstly,differences in motion characteristics of different types of aircraft are analyzed.Subsequently,the RGB algorithm is employed to transform motion-differentiated features into trajectory images that encapsulate both trajectory shape information and target motion attributes.Finally,for scenarios with limited sample data,a transfer learning strategy is introduced,fine-tuning and optimizing a pre-trained ResNet model to accomplish aircraft model classification and recognition.Experimental results demonstrate that the proposed method achieves an aircraft model classification accuracy of 75.6%on small-sample datasets,outper-forming baseline models by an improvement of 11 to 15.3 percentage points.