Abstract: Vehicle target detection is an important part of autonomous driving. Existing vehicle target detection algorithms have not fully considered the advantages and disadvantages of CNN (convolutional neural network) and Transformer in feature extraction, which to some extent limits the overall performance of the network. This paper proposes a dual branch feature aggregation network consisting of CNN and Transformer. In the encoding stage, based on the respective advantages of CNN and Transformer, a dual branch backbone network is constructed to extract the feature information of the original image. By designing a multi-level spatial attention module and a dual branch feature aggregation module, the feature information between the two branches is guided to learn from each other. Finally, a dual branch attention module is constructed to further reduce the loss of feature information in deep neural networks. In the experimental section, the effectiveness of the proposed algorithm is further verified through ablation experiments and comparative experiments. Compared to mainstream object detection algorithms, it has improved by about 3.5% in the mAP (mean average precision) metric.

Key words: vehicle target detection, convolutional neural network (CNN), Transformer, dual branch, guided learning

摘要： 车辆目标检测是自动驾驶的重要环节，现有的车辆目标检测算法在特征提取方面没有充分考虑卷积神经网络（convolutional neural network，CNN）和Transformer各自的优缺点，一定程度上限制了网络的整体性能。提出了一种由CNN和Transformer组成的双分支特征聚合网络。在编码阶段，基于CNN和Transformer各自的优势，构建了双分支主干网络来提取原始图像的特征信息；通过设计的多级别空间注意力模块和双支路特征聚合模块，使两个分支间的特征信息相互引导学习；通过构建的双分支注意力模块来进一步减少深层神经网络中特征信息的丢失。在实验部分通过消融实验和对比实验进一步验证了所提算法的有效性，其相比主流的目标检测算法，在mAP（mean average precision）指标上提升了约3.5%。

关键词: 车辆目标检测, 卷积神经网络（CNN）, Transformer, 双分支, 引导学习