为解决车辆识别系统中类型识别率低的问题，提出了一种基于改进 CenterNet 的车辆识别方法。首先，该方法采用 ResNet18 作为基础网络，以减少网络参数; 然后， 针对 CenterNet 车辆目标识别存在定位效果不理想的问题，采用带间距的交并比损失取 代 CenterNet 损失函数中的偏置损失和宽高损失，同时采用单尺度自适应空间特征融合 及自适应逐层特征融合方法，将网络的多级特征进行融合。实验结果表明: 在Vehicle 数据集上，平均精度均值提升了 1. 9 个百分点; 在 BDD100K 和 Pascal VOC 数据集上， 预测边框跟真实边框交并比为 0. 5 时的平均精度分别提升了 5. 2 个百分点和 2. 5 个百分 点; 在 GTX1080Ti 上，推理速度每秒可达 149 帧，文中提出的改进 CenterNet 能够明显 提高车辆的识别精度。

A vehicle recognition algorithm based on improved CenterNet was proposed to solve the problem of low target recognition rate in vehicle recognition system． Firstly，the algorithm used ResNet18 as the basic network to reduce network parameters． Secondly，in view of the problem that the CenterNet model has the demerits of inaccurate positioning of vehicles，it was proposed to use the distance intersection over union loss to replace offset loss and width-high loss; meanwhile，single-scale spatial feature fusion ( SASFF) method and adaptive hierarchical feature fusion ( AHFF) method were employed to fuse several feature maps of the network． The experimental results show that on the Vehicle data set，mean average precision of the improved CenterNet models increases by 1. 9% ; on the BDD100K and Pascal VOC data sets，average precision at 0. 5 of the intersection over union between the predicted boxes and the true boxes increase by 5. 2% and 2. 5% ，respectively，and the inference speed on GTX1080Ti can reach 149 f /s． The improved CenterNet proposed in this paper can significantly improve vehicle recognition accuracy．