SAR target recognition based on inverted residual and knowledge distillation

Abstract

Listen

Translate article

At present, deep convolutional neural network (CNN) has been successfully applied to synthetic aperture radar (SAR) target recognition, and achieved good recognition effect. Compared with traditional methods, the recognition performance has been significantly improved. However, in practical applications, the resources of data processing platform are very limited, the computation and the memory cost of deep convolutional neural network are high. These two factors hinder its smooth deployment on embedded devices. This paper proposed a lightweight neural network design strategy combined with knowledge distillation for target recognition. First, a convolutional network model is designed based on the improved inverted residual structure, and a lightweight neural network is obtained, which is used as a student network. Then, the teacher network (a well-trained deep network model) is used to perform knowledge distillation, which affects the student network. training to improve the recognition accuracy. Finally, the trained student network is used to complete the 10-category target recognition in the MSTAR dataset.