Design and Optimization of Deep Convolutional Neural Network for Aircraft Target Classification

Aiming at the problems of low classification accuracy and less classification types in the classification for aircraft targets by using conventional methods and neural networks, the feasibility of deep convolutional neural network (DCNN) models is studied. To match model capacity, avoid overfitting, and improve classification performance, a nine-layer DCNN model is designed and optimized with stochastic gradient descent optimizer. Six representative types of aircrafts arc selected in the datasct, and two regularization cascade methods arc proposed to prevent overfitting and speed up the model convergence. Finally, an aircraft classification accuracy of 99. 1% is achieved, which demonstrates the effectiveness of the DCNN model in aircraft target classification. By analyzing the classification results of the normalized confusion matrix, the accuracy of the self-classification of each type of aircraft is given. In addition, a group of comparative experiments arc designed to test the same datasct with the classic AlexNet. The results show that the proposed DCNN model is superior to the AlcxNet classification algorithm with an accuracy improvement of 95. 5%. This model effectively solves the problem of low accuracy in aircraft target classification at present and proves that the DCNN model has certain reference values and application prospects in the classification research of military and civil aviation aircraft targets.