联合超分与知识蒸馏的低分辨率装甲车辆识别方法

Low-Resolution Armored Vehicle Identification Approach via Joint Super-resolution and Knowledge Distillation

装甲车辆是陆战场敌我侦察打击的主要对象,受侦察距离远、侦测范围大等条件约束,侦察影像中的装甲车辆通常呈现分辨率低、细节缺失等特点,使得对装甲车辆的识别极为困难。现有基于深度学习的大模型虽然取得了较高的精度,但是缺乏对模型参数量和计算量的考虑,难以落地应用。为提升计算速度,提出了一种联合超分与知识蒸馏的低分辨率装甲车辆识别方法。通过构建联合超分与分类的轻量化模型和设计基于知识蒸馏的轻量化模型学习方法,极大地简化了模型的参数量和计算量,实现了所提轻量化模型在性能表现上与大模型相近的效果,并在3个公开数据集及自建的装甲车辆识别数据集上,验证了所提模型与方法的有效性。

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 reconnaissance images often present characteristics of low-resolution and missing details,making their identification 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 volume 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.