# Sensitive Information Detection Based on Deep Learning Models
## 2 YOLOv8 Detection Algorithm  
Since its introduction, the YOLO model has made significant achievements in the field of computer vision, particularly excelling in object detection tasks. As a key player in this field, Ultralytics released YOLOv8 on 10 January 2023, following the successes of YOLOv5 and YOLOv7. Building upon the strengths of its predecessors, YOLOv8 further enhances detection accuracy and processing speed, making it even more outstanding in real-time object detection tasks. Compared to previous versions, YOLOv8 may have undergone significant adjustments and optimizations in its algorithm structure, such as introducing more efficient network architectures, more precise object detection algorithms, and more advanced learning techniques. These improvements aim to enhance the model's ability to recognize objects of different types and sizes while maintaining or improving processing speed, thereby achieving higher practicality and accuracy in various application scenarios. Figure 1 depicts the network structure of YOLOv8. The core architecture of YOLOv8 consists of a backbone network and a neck module. In this structure, the backbone network is responsible for the initial processing of input images, extracting basic visual features through multiple convolutional layers (Conv). These convolutional layers not only capture image information at different levels but also optimize the balance between performance and efficiency through adjustments in their depth and width. The C2f module, an evolution of the C3 module, plays a crucial role in residual learning. It integrates the advantages of the ELAN structure in YOLOv7, enhancing gradient branching by reducing the number of standard convolutional layers and effectively utilizing bottleneck modules. This design maintains the lightweight nature of the model while improving the capability to capture fine details in gradient flow. After feature extraction, the SPPF module further processes the feature maps. By applying pooling kernels of different sizes, the SPPF module can aggregate features of different scales, thereby enhancing the network's adaptability to changes in target size. This makes the model more robust in detecting objects of different sizes. The neck layer of YOLOv8 adopts an FPN+PAN structure, effectively integrating features from different levels. The FPN structure enhances the model's semantic understanding of targets by combining high-level semantic information with low-level detail information. The PAN structure, on the other hand, promotes information flow between different feature layers through upsampling and downsampling techniques, further enhancing the fusion effect of features. This mechanism enables the network to perform exceptionally well in processing and fusing target features. The head utilizes features extracted from the preceding two stages to extract category and positional information of detected targets for recognition. It has been replaced by the currently popular decoupled head structure, separating the classification and detection heads. This addresses the differing focus between classification and localization. Additionally, an anchor-free object detection method is employed to improve detection speed. In terms of loss computation, a dynamic allocation strategy for positive and negative samples is utilized, with VFL Loss as the classification loss and DFL Loss+CIOU Loss as the regression loss.