基于改进残差网络的道口车辆分类方法

为了提高模型在道口环境下的车辆图像的特征提取和识别能力,提出了一种基于改进残差网络的车辆分类方法。首先以残差网络为基础模型,改进了残差块中激活函数的位置,并将残差块中的一般卷积用分组卷积代替,引入注意力机制,用焦点损失函数替换交叉熵损失函数。实验部分先用公开数据集StanfordCars进行预训练,再用自建的道口车辆数据集进行迁移学习。结果表明,改进模型在两个数据集中的准确率均优于几种经典的深度学习模型。

Classification Method of Crossing Vehicle Based on Improved Residual Network

To improve the feature extraction capability and recognition capability of models for vehicle images in crossing environments,a vehicle classification method based on an improved residual network is proposed.First,the residual network is used as the basic model,the position of the activation function on the residual block is improved,and the normal convolution in the residual block is replaced with a group convolution.An attention mechanism is then added in the residual block.Finally,the focal loss function replaces the cross-entropy loss function.In the experiment,the Stanford Cars public dataset is used for pretraining and a self-built crossing vehicle dataset is used for migration learning.The results show that the classification accuracy of the proposed model is better than several classical deep learning models in both datasets.