融合注意力机制的高效率网络车型识别

为了解决现有的车型识别算法对车型特征描述不充分的情况，提出融合注意力机制的高效率网络车型识别算法. 利用高效率网络中的复合缩放方式来平衡网络的深度、宽度和分辨率，将深度可分离卷积集成到基础特征提取模块中来提高模型准确率. 增加双通道的残差注意力机制来关注图片中的关键信息，获得含有更加丰富语义信息的特征图. 在网络的末端添加单独的softmax分类器，使用标签平滑正则化对损失函数进行处理，减小模型过拟合的问题. 在BIT-Vehicles数据集上进行实验，结果表明，提出方法的平均分类准确率为96.83%，较改进前的模型提高了1.11%，优于现有DCNN、Faster-CNN的改进算法，较Faster R-CNN提升了7.16%.

Efficient network vehicle recognition combined with attention mechanism

An efficient network vehicle recognition algorithm combined with attention mechanism was proposed in order to solve the problem that the existing vehicle type recognition algorithm does not adequately describe the vehicle type characteristics. The depth, width and resolution of the network were balanced by the compound scaling method in the efficient network, and the depth separable convolution was integrated into the basic feature extraction module in order to improve the accuracy of the model. The residual attention mechanism of two channels was added to pay attention to the key information in the picture, and the feature map with richer semantic information was obtained. A separate softmax classifier was added at the end of the network, and the label smoothing regularization was used to deal with the loss function in order to reduce the problem of model over-fitting. Experiments on BIT-Vehicles data set showed that the average classification precision of the proposed method was 96.83%, which was 1.11% higher than that of the original model, and was better than the existing improved algorithms of DCNN and Faster-CNN and 7.16% higher than Faster R-CNN.