基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。

Fine-grained vehicle recognition under multiple angles based on multi-scale bilinear convolutional neural network

In view of the problem that it is difficult to accurately recognize the type of vehicle due to scale change and deformation under multiple angles, a fine-grained vehicle recognition model based on Multi-Scale Bilinear Convolutional Neural Network (MS-B-CNN) was proposed. Firstly, B-CNN was improved and then MS-B-CNN was proposed to realize the multi-scale fusion of the features of different convolutional layers to improve feature expression ability. In addition, a joint learning strategy was adopted based on center loss and Softmax loss. On the basis of Softmax loss, a category center was maintained for each category of the training set in the feature space. When new samples were added in the training process, the classification center distances of samples were constrained to improve the ability of vehicle recognition in multi-angle situations. Experimental results show that the proposed vehicle recognition model achieved 93.63% accuracy on CompCars dataset, verifying the accuracy and robustness of the model under multiple angles.