## 1 Introduction  
Target detection technology based on deep learning is widely used in unmanned driving, intelligent monitoring, intelligent transportation and other fields. For example, with the traffic violation of statutes increased, monitoring and accurately detecting vehicles in videos has become an important research topic of city traffic management. The target detection algorithm based on deep learning in traffic attracts the attention of many researchers, but for complex traffic monitoring environments for different scales and types of vehicles, quick and accurate target detection has been by far one of the most challenging tasks [1; 2]. In recent years, researchers at home and abroad have carried out extensive and in-depth research on target detection methods based on deep learning. The R-CNN (Region-CNN) model proposed by [PERSON] et al. [3] generates a large number of candidate boxes by selective search algorithms [4], and then candidate boxes are used as the input to the convolutional neural network for detection. However, the process of generating candidate boxes is time consuming, and there is a large amount of overlap between the candidate boxes. The improved Fast R-CNN algorithm [5] adds target classification and regression into the neural network, reducing the operation time. [PERSON] et al. put forward the concept of anchor boxes in Faster R-CNN [6] and realized the generation of candidate boxes through convolutional neural network, thus effectively reducing the time of target detection. However, all of the above algorithms generate candidate targets through candidate boxes, and then use convolutional neural network for processing. Although the detection accuracy is high, it cannot meet real-time requirements. In other words, all of the above methods are two-stage detectors. In order to solve the real-time problem of target detection, [PERSON] et al. proposed the YOLO (You Only Look Once) algorithm [7] and introduced a one-stage detector, which for the first time treated target detection as a regression problem, and directly predicted the category and position of targets through the convolution neural network without generating candidate boxes. Although it improves the detection speed, the accuracy is also low. Subsequently, [PERSON] proposed an improved YOLO v2 algorithm [8] and applied all normalization layers in the convolutional layer [9], which improved the recall rate and positioning ability of the target. [PERSON] et al. proposed the SSD algorithm [10], which was faster and more accurate than the previous YOLO algorithm. However, due to the extraction of features through low-level convolution, there was a problem of insufficient feature extraction. Later, based on YOLO v2, [PERSON] proposed the YOLO v3 algorithm [11], which adopted the Darknet53 network structure and predicted with multi-scale features to improve the detection accuracy of small targets. Meanwhile, it also improved the detection accuracy while maintaining real-time performance. On the basis of YOLO v3 algorithm, [PERSON] et al. proposed YOLO v4 algorithm [12], which added CSPDarknet53, the Mish activation function and dropblock into the backbone network. The detection performance of objects is better than YOLO v3. However, due to the complex and varied environmental background and the shield between vehicles, there are still many problems in the practical application of vehicle detection, and the deep learning model for vehicle detection in complex environments is still a challenging subject. This paper proposes the one-stage detector algorithm based on background prediction and group normalization for vehicle detection algorithms, which can improve the accuracy of the vehicle detection in complex environments. The contribution of this paper is as follows:  
1. Adding a network module to adjust the width and height of anchor boxes and predict target backgrounds, which firstly detects the environment background to prevent the vehicle from being affected by the environmental background.