Aircraft image classification based on improved YOLOv8

Aircraft image classification has important application value in military, civil aviation and UAV surveillance. In recent years, YOLOv8 has performed well in many tasks as an advanced target detection model, but there is still room for improving its classification accuracy in specific tasks. In this paper, we enhance the YOLOv8 model for aircraft image classification by integrating the ConvNeXt module into its backbone network and incorporating the Coordinate Attention (CA) mechanism, thereby improving feature extraction and spatial perception capabilities. The ConvNeXt module significantly improves the efficiency of feature extraction through deep convolution operation and feature fusion strategy; The CA enhances the model's ability to capture spatial features by incorporating coordinate information effectively. In this paper, experiments are conducted on six types of aircraft image datasets, including in-flight UAVs, fighters, helicopters, missiles, airliners, and rockets. The experimental results show that the average classification accuracy of the improved model on the test set is improved by 3.5%. This indicates that the improvement strategy of introducing the ConvNeXt module and CA attention mechanism is effective in improving the classification performance of YOLOv8. The study in this paper not only verifies the effectiveness of the proposed method, but also provides new ideas and methods for future research on aircraft image classification.