关键词: 车辆检测, 类型识别, 深度学习, 特征融合

Abstract: 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．

Key words: vehicle recognition, target detection, deep learning, feature fusion