基于小波特征与注意力机制结合的卷积网络车辆重识别

Convolutional network-based vehicle re-identification combining wavelet features and attention mechanism

针对现有的基于卷积神经网络(CNN)的车辆重识别方法所提取的特征表达力不足的问题,提出一种基于小波特征与注意力机制相结合的车辆重识别方法。首先,将单层小波模块嵌入到卷积模块中代替池化层进行下采样,减少细粒度特征的丢失;其次,结合通道注意力(CA)机制和像素注意力(PA)机制提出一种新的局部注意力模块——特征提取模块(FEM)嵌入到卷积网络中,对关键信息进行加权强化。在VeRi数据集上与基准残差网络ResNet-50、ResNet-101进行对比。实验结果表明,在ResNet-50中增加小波变换层数能提高平均精度均值(mAP);在消融实验中,虽然ResNet-50+离散小波变换(DWT)比ResNet-101的mAP降低了0.25个百分点,但是其参数量和计算复杂度都比ResNet-101低,且mAP、Rank-1和Rank-5均比单独的ResNet-50高,说明该模型在车辆重识别中能够有效提高车辆检索精度。

Aiming at the problem of insufficient representation ability of features extracted by the existing vehicle re-identification methods based on convolution Neural Network(CNN),a vehicle re-identification method based on the combination of wavelet features and attention mechanism was proposed.Firstly,the single-layer wavelet module was embedded in the convolution module to replace the pooling layer for subsampling,thereby reducing the loss of fine-grained features.Secondly,a new local attention module named Feature Extraction Module(FEM)was put forward by combining Channel Attention(CA)mechanism and Pixel Attention(PA)mechanism,which was embedded into CNN to weight and strengthen the key information.Comparison experiments with the benchmark residual convolutional network ResNet-50 and ResNet-101 were conducted on VeRi dataset.Experimental results show that increasing the number of wavelet decomposition layers in ResNet-50 can improve mean Average Precision(mAP).In the ablation experiment,although ResNet-50+Discrete Wavelet Transform(DWT)has the mAP reduced by 0.25 percentage points compared with ResNet-101,it has the number of parameters and computational complexity lower than those of ResNet-101,and has the mAP,Rank-1 and Rank-5 higher than those of ResNet-50 without DWT,verifying that the proposed model can effectively improve the accuracy of vehicle retrieval in vehicle re-identification.