融合多级注意力机制和信息融合的车型识别

不同车型类间外观特征高度相似，同车型类内外观差异大，这对特征提取网络提出了更高的要求。现有的车型识别方案仅依靠车辆外观特征识别，整体识别准确率不高。为此，首先在主干网络设计了多级注意力机制，提高主干网络对车型特征提取和识别能力；其次根据卡口环境下不同车辆位置车辆外观特征的变化提出了车辆位置和外观特征融合结构，从而提取出融合位置信息的复合图像特征，减小类内特征距离，增强主干网络所提取的特征的表达力和稳健性；最后在分析了难例样本注意力热力图基础上，对难例样本注意力区域进行干预，使网络聚焦于车辆细小差异的局部区域。实验结果表明，本文所提出的车型识别方法整体性能比现有方案有显著提升。

Research on vehicle type recognition based on multilevel attention mechanism and information fusion

The appearance characteristics of different models are highly similar and different from those of the same model, which poses a great challenge to the feature extraction network.Existing vehicle type classification schemes only rely on vehicle appearance feature recognition, and the overall recognition accuracy is not high.Therefore,firstly, this paper designs a multi level attention mechanism in backbone network to improve the ability of main network to extract and recognize vehicle features. Secondly, according to the changes of vehicle appearance characteristics at different vehicle locations in the bayonet environment, a feature fusion structure of vehicle location and appearance features is proposed, which extracts the composite image features of the fusion location, reduces the feature distance within the class, and enhances the expressiveness and robustness of the features extracted by the main network.Finally, based on the analysis of the attention heat map of difficult samples, the attention area of difficult samples is intervened to make the network focus on the local area of small differences between vehicles. The experimental results show that the overall performance of the vehicle type recognition method proposed in this paper is significantly improved than the existing scheme.