无人机目标分类的深度卷积网络设计与优化

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

针对传统无人机目标分类方法效率低、特征提取能力不足和适应性差等问题,通过对无人机自身特点和现有分类方法的分析,提出了引入注意力机制优化深度卷积神经网络的无人机分类方法.设计多组对比实验,根据实验效果设计出模型结构为3层卷积层、3层池化层、2层全连接层的卷积神经网络进行训练,得到最优的无人机目标分类模型,再引入卷积注意力模块对特征图元素进行加强和抑制,引入批归一化层加速模型收敛,提升泛化能力.实验结果表明:引入卷积注意力模块和批归一化层优化后的无人机目标分类模型的识别率达到92.44%,较优化前提升1.5%,相比于其它神经网络模型具有识别率高、收敛速度快的优点,可以基本满足实际场景中无人机目标分类的要求.

Aiming at the problems such as low efficiency,limited ability of feature extraction,and poor adaptability of traditionalclassification methods for UAV targets,this study proposes a UAV classification method that introduces attention modules to optimize deep convolutional neural networks by analyzing the characteristics of UAVs and existing classification methods.Multiple sets of comparative experiments are designed for a model structure of a convolutional neural network with three convolutional layers,three pooling layers,and two fully connected layers according to the experimental results for training to obtain the optimalclassification model for UAV targets.Then,the convolutional block attention module is introduced to strengthen and suppress feature map elements,and the batch normalization layer is introduced to accelerate convergence and improve generalization capabilities of the model.Experimental results show that after introduction of convolution block attention modules and batch normalization layers,the recognition rate of the classification model for UAV targets rises by 1.5%to 92.44%.Its advantages of high recognition rate and fast convergence over other neutral network models can basically meet the requirements of UAV target classification in actual scenes.