# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 1 Introduction  
With the continuous development and intelligence of urban transportation, the safety and efficiency of autonomous driving have become a focal point for researchers [1; 2; 3; 4]. Visual traffic monitoring systems, as an essential component of traffic management and safety, have broad application prospects. These systems [5; 6; 7; 8] utilize cameras and other sensor devices to capture real-time traffic scenes and analyze and process images or videos using computer vision algorithms to extract traffic information and key data. Visual traffic monitoring systems consist of functions such as vehicle detection, vehicle tracking, license plate recognition, and behavior analysis, which are used to extract traffic information and features. Furthermore, they enable functions such as vehicle count, traffic flow monitoring, congestion detection, and traffic accident warning through the utilization of traffic information and features [9; 10]. Visual traffic monitoring systems have extensive applications in traffic management, urban planning, and traffic safety. They can assist traffic management departments in better monitoring and managing traffic flow, improving road utilization efficiency, reducing traffic congestion and the probability of accidents, and providing a safer and more convenient traffic environment for urban residents. Traditional traffic monitoring systems primarily rely on 2D image processing [11; 12; 13], which can only provide limited information, thereby limiting the accurate recognition and stereo perception capabilities of traffic targets. To address this issue, in recent years, researchers have started to incorporate 3D object detection techniques from the computer vision field into roadside monitoring viewpoints to provide more comprehensive and accurate traffic object information [14; 15]. Single-camera 3D traffic object detection, as one of the methods, estimates the three-dimensional positions and orientations of trafficobjects based on the texture and contextual information in the images, in the case of a single camera. There are several advantages to using single-camera 3D object detection in roadside traffic monitoring systems:  
1. Spatial perception: By using 3D detection, the system can accurately perceive the precise position, size, and orientation information of vehicles, enabling accurate perception of vehicles in three-dimensional space. This helps to better understand vehicle motion behavior and spatial layout. 2. Distance estimation: 3D detection provides distance estimation between vehicles and monitoring cameras. This is crucial for evaluating key parameters such as distance, speed, and acceleration between vehicles and cameras. Such information is vital for traffic flow statistics, behavior analysis, and accident warning applications. 3. Enhanced safety: Through 3D detection, the monitoring system can more accurately identify the position and motion status of vehicles, providing more reliable traffic information. This helps in the timely detection of traffic violations, abnormal driving behaviors, and accident risks, enabling appropriate warnings and measures to be taken and improving road safety. 4. Cost-effectiveness: Compared to multi-sensor fusion solutions, monocular vision systems have lower costs. They only require a single camera to capture image information without the need for additional sensor devices, thereby reducing installation and maintenance costs. However, the use of a monocular 3D object detection method requires precise camera intrinsic and extrinsic parameters during the model training and deployment process, and the dataset annotation process is more cumbersome. However, these limitations cannot overshadow the advantages of this algorithm. Moreover, single-camera 3D object detection faces several challenges and difficulties. Firstly, cameras in roadside monitoring systems have different installation heights, pitch angles, and complex road environments.