## 2 Related Work  
Deep learning target detection algorithms have been widely applied to vehicle detection of urban traffic monitoring. Meanwhile, improved algorithms for specific problems have been proposed and achieved remarkable results.  
The region-based vehicle detection algorithm firstly produces the candidate box in the image and then classifies the vehicle in the candidate box. [PERSON] et al. [13] proposed a unified rapid vehicle detection framework (DAVE), which effectively combined vehicle detection and attribute marking. DAVE is composed of two convolution neural networks: a vehicle candidate box extracted network and a vehicle inference learning network, to predict the angle of view, the properties of color and type. The improved network can effectively detect the traffic monitoring vehicles, but DAVE has a poor handling effect on small vehicles with occlusion and slow detection speed. [PERSON] et al. [14] proposed a fast deep neural network with knowledge guidance training and prediction of regions of interest, which can significantly reduce overall computational complexity and improve vehicle detection performance. Compared with the traditional SSD algorithm, the detection speed of this method is significantly improved, but the detection accuracy is not significantly improved.  
The vehicle detection algorithm based on regression uses a one-stage neural network to directly predict the position and category of vehicles in the image and realize real-time detection. [PERSON] et al. [15] proposed a traffic camera data set (MIO-TCD), including 11 traffic object classes, to evaluate traffic vehicle detection algorithms. In order to solve the problem that convolution features are mesoscale sensitive in vehicle detection tasks, [PERSON] et al. [16] proposed a scale-insensitive convolutional neural network (SINet) for rapid detection of vehicles with large-scale changes. SINet as a one-stage detector can improve accuracy and speed in KITTI datasets, and the detection performance of SINet for small scale vehicles is still to be improved due to the large number of highly overlapping, fuzzy, and small vehicles in practical application scenarios.  
The structure of one-stage detectors is shown in Figure 1, which consists of an input terminal, a backbone network module, a characteristic enhancer module and a prediction module. As the backbone network of feature extractor, VGG16, ResneXt-101 [17], and Darknet53 can achieve the feature extraction of the input image. In order to better fuse features, the Neck module was at the back of the backbone network, which can use FPN [18], PANet [19] and Bi-FPN algorithms. Because different targets have different characteristics, using the characteristics of shallow can distinguish some simple targets, and using the characteristics of deep can distinguish the complex target. Then the characteristics of different outputs can better deal with large targets and small targets. Finally, RPN, RetinaNet and FCOS were used in the prediction module to predict the position and category of images. The multi-scale prediction of the one-stage detector was be carried out on the feature map with the size of \(19\times 19\), \(38\times 38\) and \(76\times 76\). However, before the feature map outputs the prediction results, the network carried out the feature fusion operation first, and splintered the features with high semantic, low resolution and low semantic, high resolution together so that the features with high resolution also contain rich semantic information. For the three output feature graphs, each pixel predicts three boxes on the feature graph, and each box predicts the center coordinates \(x\), \(y\), \(h\), \(w\) and the object confidence. Finally, the non-maximum suppression (NMS) algorithm can select the predicted bounding box as the final detection box.