Low-Resolution Armored Vehicle Identification Approach via Joint Super-resolution and Knowledge Distillation
Armored vehicles are the main targets for reconnaissance and strikes on land battlefields.Due to constraints such as long reconnaissance distance and large detection range,armored vehicles in re-connaissance images often present characteristics of low-resolution and missing details,making their iden-tification extremely difficult.Although existing large models based on deep learning have achieved higher accuracy,they lack consideration for model parameters and computational complexity,making it difficult to implement and apply them.In order to improve computational speed,a low-resolution armored vehicle recognition approach via joint super-resolution and knowledge distillation is proposed.By constructing a lightweight model that integrates super-resolution with classification and designing a learning method for lightweight models based on knowledge distillation,the model's parameter quantity and computational vol-ume have been greatly simplified.The proposed lightweight model demonstrates performance similar to that of the larger model.The effectiveness of the proposed model and method were verified on the three public datasets and a self-built armored vehicle recognition dataset.