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

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