Research on Aircraft image recognition based on transfer learning and improved YOLOv5 model

Effective differentiation of aircraft types using images is important for providing military combat information as well as civilian aircraft operations. Aircraft image recognition has many difficulties such as large variations of target scale, complex backgrounds, and difficult data set acquisition, which lead to the low recognition accuracy of existing models. To address the problem of low recognition accuracy caused by the above difficulties. This paper proposes the improved YOLOv5 model for the recognition of aircraft images. First, this paper designs the CSPResNet50dCA network as the backbone of the YOLOv5 model to enhance the feature extraction capability for small target aircraft in images. By introducing the coordinate attention mechanism and the CSP structure, the feature-focusing capability and the computing speed of the model are enhanced. Afterward, we use data enhancement to expand the data set and transfer learning to improve the generalization ability and convergence speed of the model, so as to improve its robustness. The experimental results show that the improved YOLOv5 model has significantly improved the recognition accuracy of aircraft targets， and significantly enhanced feature extraction ability for small target aircraft with good generalization ability.