## 3 The Neural Network Dynamic Detection Model
### Monocular Vision Distance Measurement  
Distance measurement methods based on machine vision primarily include binocular vision, monocular vision, and structured light vision techniques. The structured light vision method has numerous constraints regarding matching scenes, heavily relies on objective environments, and is significantly affected by lighting conditions. The binocular vision method requires calibration of internal and external parameters for multiple sensors and conversion of spatial coordinate systems. Despite its high precision and ability to obtain depth information, it is greatly influenced by the feature-matching process. Variations in parameters and the matching of synchronously captured images can lead to decreased accuracy [27]. On the other hand, the monocular vision measurement method can avoid these issues, offering a simpler structure and faster computational speed. Moreover, the cost of the widespread installation of monocular cameras is lower compared to the first two methods. Current monocular vision distance measurement techniques [28] primarily include the following methods: one involves using camera model parameters for coordinate system conversion, with accuracy highly dependent on camera parameter calibration. This method primarily utilizes internal camera parameters and principles of perspective projection geometry to deduce the relationship between the image coordinate system and the world coordinate system. Another method is mathematical regression, which establishes a regression model based on the relationship between the position of the target in the image under different states and the actual distance, with the need for extensive data increasing the difficulty of estimating distance information. Additionally, there is a method based on the imaging principle of the camera and the relationship of similar triangles, using actual distance information, distance information in the image, and the camera's focal length to establish a proportional relationship. Compared to the first two methods, the monocular ranging model based on camera imaging principles features a simple structure and high accuracy, facilitating easy deploy  
Figure 4: Workflow of integrating background subtraction algorithms with the YOLO model. ment and widespread application. This study will employ the method depicted in Figure 5 for capturing the dimensional information of the target. Measuring the width of an object using monocular vision involves converting the pixel dimensions in the image to actual physical dimensions. This conversion depends on multiple factors, including the camera's internal parameters (such as focal length), the distance between the camera and the object, and the resolution of the image itself. In the absence of depth information (provided by stereo vision systems or depth cameras), monocular vision systems often require additional information or assumptions to estimate dimensions. Here is a commonly used method for estimating the dimensions of objects based on monocular vision, assuming knowledge of the camera's internal parameters and the distance from the camera to the object through related calibration work:  
1. Camera Model: Focal Length \((f)\): The focal length of the camera lens, is typically expressed in millimeters, but it needs to be converted into units consistent with the image resolution for calculations. Sensor Size \(\left(S_{x},S_{y}\right)\): The actual dimensions of the camera sensor in both horizontal and vertical directions, usually measured in millimeters. Image Resolution \((W,H)\): The pixel dimensions of the image, i.e., the number of pixels in both horizontal and vertical directions. 2. Object Size Estimation: Assume you know the actual distance \((D)\) from the camera to the object and the pixel width \(\left(w_{pixels}\right)\) of the object in the image. 3.