## 2 Related Works  
The algorithms for human detection in computer vision can be categorized into traditional machine learning-based methods and novel deep learning-based methods [20]. Traditional machine learning methods rely on detection algorithms that manually extract image features to detect targets [11]. [PERSON] et al. enhanced widely used edge-based features through texture and color information and employed a partial least squares (PLS) analysis to achieve human localization and tracking [21]. However, this enhancement resulted in an extremely high-dimensional feature space. [PERSON] et al. applied the rotating histogram of oriented gradient (RHOG) algorithm and machine learning-based SVM classifier in a top-down view to significantly improve detection performance [22]. However, this method has the drawbacks of traditional machine learning, as its multi-level operations cannot achieve good real-time speed and detection accuracy, making it difficult to apply in practical scenarios. Deep learning-based methods are categorized into two-stage and one-stage algorithms, which are known for their real-time performance and detection accuracy. In multi-stage detectors like the R-CNN series [15; 23; 24; 25], one model identifies object regions while another model classifies and detects object positions [23]. Although these methods yield high accuracy, they suffer from increased computational costs and longer processing times. In contrast, one-stage algorithms like YOLO [13; 26; 27; 28; 29] and SSD series [14; 30] segment images into grids, predicting object categories and bounding box coordinates directly without intermediate tasks. They excel in agility and real-time capabilities. [PERSON] et al. integrated the Residual-101 classifier with SSD to create DSSD [30], thereby enhancing context in object detection. Similarly, [PERSON] et al. combined efficient training tools with YOLOv7 [27], thereby achieving superior speed and accuracy (SFPS to 120 FPS) compared to existing detectors. While one-stage algorithms may slightly lag in accuracy compared to two-stage algorithms, they offer notable advantages in speed and real-time performance. Launched by Ultralytics in 2023, YOLOv8 is currently a research hotspot and widely used in the field of indoor object detection. The network structure of YOLOv8 is shown in Figure 1. [PERSON] et al. applied CNN to extract features from the detection objects cropped by the YOLOv8 object detection algorithm and then integrated these features into a single feature vector using LSTM to achieve position localization of multiple indoor targets [31]. [PERSON] et al. enhanced the YOLOv8 model's ability to detect small targets and specific motion detection in various visual environments by focusing on large-sized feature maps and introducing Bi-PAN-FPN [32]. Although this method has generalization in various visual environments, it does not take into account that indoor scenes often contain dense elements and small-scale features of different scales. [PERSON] et al. proposed an intelligent monitoring method for the real-time distribution of indoor pedestrians based on deep learning and spatial partitioning [33]. Enhanced YOLOv8 and DeepSORT models were employed to intelligently generate pedestrian IDs and location data, facilitating evacuation count and direction determination. This approach surpasses alternative algorithms in detection accuracy and efficiency. However, further experiments and evaluations are required to assess its performance adequately, considering factors like camera resolution, monitoring distance, passenger occlusion, and environmental brightness that impact detection accuracy. Unlike the outdoors, indoor environments are characterized by different lighting conditions, potential obstacles, such as furniture, and enclosed spaces, all of which can seriously affect the accuracy and quality of human detection [34]. [PERSON] et al. proposed a novel real-time face detector, YOLO-FacedV2, based on the YOLOv5 architecture [9].