## 2 YOLOv8 Framework  
The YOLOv8 model, introduced by Ultralytics in early 2023 as part of the YOLOv5 series, represents a significant advancement in the You Only Look Once (YOLO) family of object detection models. Building upon the efficient, real-time detection capabilities of its predecessors, YOLOv8 incorporates several technical innovations that markedly enhance its performance in object detection tasks [18; 19].  
The model employs a novel backbone network, a refined loss function, and an anchor-free detection head, resulting in notable improvements in precision, speed, and flexibility. Particularly in real-time detection scenarios, YOLOv8 is capable of delivering outstanding performance even under limited computational resources.  
As illustrated in Figure 1, the YOLOv8 architecture is composed of three primary parts: the backbone, the neck, and the head. The backbone employs the CSPDarkNet structure [20], which consists of various layers such as Conv, C2f, and Spatial Pyramid Pooling Fast (SPPF) to extract image features. The Conv layers are responsible for extracting fundamental features, utilizing SiLU activation function alongside batch normalization to normalize feature representations. The C2f module further captures detailed features to enhance the model's ability to discern finer details, while the SPPF layer aggregates information from different scales through multi-scale pooling, strengthening the model's robustness when detecting objects of varying sizes.  
Following the backbone, the neck section, comprising the Upsample, Concat, and C2f modules, is designed to further extract and fuse multi-scale feature maps, which bolsters the model's performance in multi-scale object detection tasks. The Upsample layer enlarges low-resolution feature maps to align them with high-resolution ones, while the Concat layer merges features from different levels, enabling the model to simultaneously process large, medium, and small objects. The C2f module further integrates multi-scale information to ensure that feature maps carry rich contextual information, ultimately enhancing detection performance. Lastly, the head section, composed of Conv, Concat, C2f, and detect layers, processes the features further and outputs the object's position and class information. The detect layer, serving as the output layer, generates the final bounding boxes and class labels with high accuracy and localization capability.  
YOLOv8 demonstrates outstanding performance across various object detection tasks and is currently one of the state-of-the-art (SOTA) models especially suited for efficient detection in resource-constrained environments [21]. Thanks to its structural innovations, YOLOv8 can maintain excellent detection precision and speed in real-time applications, making it suitable for edge computing, intelligent security, autonomous driving, and other scenarios that demand high responsiveness [22; 23; 24]. Despite its superior performance in general detection tasks, YOLOv8 exhibits certain limitations in specific application scenarios, such as the infrared image detection of electrical equipment, where it struggleswith detecting distant and small objects [25]. This limitation primarily arises from the low contrast and high noise background characteristics of infrared images. To address these challenges, this study introduces structural modifications and optimizations to the base YOLOv8 model, aimed at improving its detection performance to better meet the engineering requirements of infrared image detection in electrical equipment monitoring.