## 3 Methods
### Analysis of the Utilization of Rest Area Service Facilities and Visitor Age in Tourist Attractions  
In the analysis of facility usage in scenic area rest areas, the YOLOv8 model is employed for tourist location identification. YOLOv8 is a deep learning-based object detection algorithm that frames object detection as a regression problem. It predicts the position and category of objects in a single forward propagation step, making it suitable for various visual AI tasks, including detection, segmentation, pose estimation, tracking, and classification. The model is highly efficient and widely applicable. The YOLOv8 network model primarily consists of three components: the backbone feature extraction module (Backbone), the feature enhancement module (Neck), and the detection module (Detect), as illustrated in Figure 3. The backbone network structure employs the C2f module, which enhances gradient flow and reduces computational complexity, thereby improving the model's computational efficiency and performance. The detection head adopts a decoupled-head structure (Decoupled-Head), separating the classification and detection heads to enhance detection flexibility and accuracy. For loss calculation, the model utilizes the TaskAlignedAssigner positive sample assignment strategy and incorporates Distribution Focal Loss (DFL Loss) and CIOU Loss as regression losses. This optimizes the loss function computation, further enhancing the model's training effectiveness and detection accuracy [26; 45]. The YOLOv8 pre-trained model used in this study was trained on the COCO training set (including 80 classes) with the following hyperparameter settings: a learning rate of 0.01, 100 epochs, and a batch size of 16. The model achieved a mean Average Precision (mAP) of 37.3. In this study, the range of service facilities was first outlined on the input images collected. Based on the fact that the seat height of a bench is usually about 40 cm above the ground, combined with the fact that the height of a person's upper body when seated is usually between 40 and 90 cm, the position of the center point of the figure frame is usually between 40 and 65 cm. When children sit down, their legs cannot fully touch the ground, so the center of the frame will be higher than the height of the chair. In addition, considering the influence of some people's height and sitting posture, we set the range of the service facility as a rectangular area with the length of the bottom edge of the chair surface and a height of 40 cm. Next, the YOLOv8 model was used to detect tourists in the image and generate a character bounding box for each tourist. After that, it was determined whether these visitors were using the facility or not. Adults sit at a height of about 0.8 m to 1.0 m, a width of about 0.4 m to 0.5 m, and a height-to-width ratio of about 1.6 to 2.5; children sit at a height of about 0.5 m to 0.7 m, a width of about 0.3 m to 0.4 m, and a height-to-width ratio of about 1.2 to 2.3; and when an adult is standing up, the height-to-width ratio is about 3.2 to 4.5. Combining the above data on human body structure and the analysis of experimental data, we set that the visitors using the facilities need to meet two conditions:  
1. The center point of the visitor's character frame needed to be located within the range of the set service facilities. 2. The height-to-width ratio of the visitor's frame had to be less than 2.5 times. A character frame with a height-to-width ratio greater than 2.5 was considered to be a person passing by the service facility and was excluded. The method of judging the utilization of service facilities in scenic open spaces is shown in Figure 4.  
Figure 4: Method for determining tourist usage of service facilities. Figure 3: YOLOv8 model structure. Passing tourists who obstructed seated tourists or interfered with the camera view, as well as pedestrians obstructing the lens, may have resulted in the detection of fewer tourists using the facilities than the actual number.