Based on the YOLOv2 algorithm, the YOLOv2-voc network structure is improved according to the actual road-scene change. The classification training model is obtained based on ImageNet data and fine-tuning technology and in accordance with the analysis of the training results and target vehicle characteristics. Consequently, the improved vehicle identification classification network structure YOLOv2-voc_mul is obtained. Using samples from simple and complex backgrounds, experiments are conducted to verify the validity of the detection method. Further, the proposed model is compared with the YOLOv2, YOLOv2-voc, and YOLOv3 models after 70000 iterations. Results show that under simple background, the improved YOLOv2-voc_mul model has an accuracy of 99.20% and the mean average precision of different models achieves 89.03%. Under complex background, the improved YOLOv2-voc_mul model has average accuracies of 92.21% and 89.44% for the single- and multi-target detection of four different models, respectively. The proposed model shows excellent accuracy, small false detection rate, and good robustness.