The traditional YOLOv3 network structure has poor robustness in extracting features such as over exposure or dark light, which leads to low recognition rate. A Dense-YOLOv3 model for traffic vehicle classification is proposed. The model integrates the characteristics of dense convolutional neural network DenseNet and YOLOv3 network, which strengthen the vehicle model feature propagation and reuse between convolution layers, and improve the anti-overfitting performance of the network. At the same time, the target vehicle is detected at different scales, and the cross-loss function is constructed to realize the multi-objective detection of the vehicle model. The model is trained and tested on BIT-Vehicle standard data sets. The experimental results show that the average accuracy of the model based on Dense-YOLOv3 vehicle detection reaches 96.57% and the recall rate is 93.30%, which indicates the effectiveness and practicability of the model for vehicle detection.