Abstract: Traffic object recognition and location have always been one of the most important topics in the field of unmanned driving and video surveillance. In this paper，an end-to-end convolution neural network vehicle detection model （HyperLocNet） was proposed for vehicle detection in traffic images surveillance. Firstly，the region proposal network（RPN）was used to generate initial candidate boxes，and the dynamic probabilistic location model was used to provide the exact location of the target in the area centered on the initial candidate box，so as to improve the stability and convergence of the location model. The dynamic probabilistic model provides rich information for accurate positioning by outputting the probability of the target in searching area. On this basis， HyperLocNet integrated training object location and recognition task to achieve end-to-end detection， improved positioning accuracy and detection efficiency，carried out experiments on traffic object data collected in surveillance video. The proposed model has good detection performance，achieves the detection speed of 13 frames per second，which has the potential of real-time processing.

Key words: traffic image, vehicle detection, dynamic probabilistic location model, convolution neural network（CNN）