## 1 Introduction  
With the dramatic improvement of people's standards of living, the rapid expansion of cities, and the growing number of private cars, traffic congestion has become an important issue that restricts urban development and affects the quality of life. In the intelligent transportation system, accurate traffic predictions can provide the basis for making a decision on urban traffic management and for finding the optimization of transportation facilities. For that, the accurate prediction of traffic transportation is an important part of developing the smart transportation system of the modern intelligent city. The rapid detections of vehicles in traffic images or videos are the main tasks of urban traffic prediction, for that, investigating an algorithm with capabilities of real-time computation and correct detections of vehicles are very important [1]. The traditional target detection methods are as follows: [PERSON] et al. proposed a featured operator, which can extract features from the region of interest selected on the images and implement target detection by training a classifier [2]. This method will greatly reduce the detection accuracy because the scene is a little complicated. [PERSON] et al. proposed an optical target detection, which is based on the methods of optical flow and the inter-frame differences [3]. The accuracy of the optical flow method is ideal but it has the problem of lower detection speed. The inter-frame difference method is fast but the accuracy is not ideal. [PERSON] et al. had proposed a sliding window classification method, which first extracts the features of the region of interest through sliding windows and then performs classification by a support vector machine (SVM) classifier to achieve target detection [4]. This method has a large amount of calculation, which leads to a slower detection speed [5]. [PERSON] et al. proposed an edge-based object detection method, which is susceptible to interference from background and noise and leads to an increase in inaccurate detections [6]. The above traditional target detection methods are not targeted at the sliding window area of selection strategies, they need a large number of calculations, result in a slow detection speed, and the area feature extraction has no generalization. In recent years, with the rapid developments of computer vision and artificial intelligence technologies, object detection algorithms based on deep learning have been widely investigated. Among them, convolutional neural networks have a strong generalization of the feature extraction of images and are convenient [7]. At present, there are two main methods of target detection in deep learning: one is a target detection algorithm combining convolutional neural networks and candidate region suggestions, represented by region-based convolutional neural networks (R-CNN) [8] and spatial pyramid pooling (SPP)-net [9]. The other is to use the series of the Single Shot MultiBox Detector (SSD) [10] and the You Only Look Once (YOLO) model [11; 12; 13] as the representative detection algorithms to convert the target detection problem into the regression problem by machine learning. The R-CNN algorithm uses a selective search to select the region suggestion box, which improves the accuracy of target detections. However, a large number of repeated calculations lead to a long time and the candidate box is scaled, which easily results in the loss of image feature information. The SPP-net algorithm proposes a pyramid pooling layer, which solves the problem of the size of the fixed input layer of the network in R-CNN, but its training steps are cumbersome and each step will generate a certain ratio of errors because the convolutional neural network and SVM classification need to be trained separately. As a result, the training takes a long time and a large number of feature files need to be saved after training, which occupies a large amount of hard disk space.