## 2 Materials and Methods
### YOLOv9 Algorithm  
YOLOv9 [23] introduces the concept of Programmable Gradient Information (PGI) and designs a supplementary supervision structure that incorporates PGI. As shown in Figure 3, the framework consists of featuring three essential components: the primary branch, a supplementary reversible branch, and a multi-level framework During the logical conclusion phase, the network only utilizes the main transition toward avoid increasing inference costs. However, during training, the complementary reversible route provides reliable gradient-based information for help the primary pathway learn effective features, preventing the loss of important feature information. This design ensures that the loss function is computed using the full input data, thereby supplying stable and reliable gradient feedback for fine-tuning the model parameters.  
In addition, the research team proposed a newly designed, low-complexity network architecture, the General Efficient Layer Aggregation Network (GELAN), as demonstrated in Figure 4c. The General Efficient Layer Aggregation Network (GELAN) is built upon the Efficient Layer Aggregation Network (ELAN) architecture in Figure 4b by integrating the Cross-Stage Partial Network (CSPNet) from Figure 4a. Firstly, balancing computational complexity and detection accuracy is at the core of the design. GELAN optimizes the model's computational requirements through modular design, ensuring that even under limited computational resources, high detection accuracy can still be maintained. Secondly, lightweight convolution operations are introduced between each convolutional layer, reducing computation, which is particularly suitable for edge devices or low-power environments, thus lowering the computational cost during inference and improving processing speed. The authors also improve computational efficiency by incorporating the Cross-Stage Partial Network (CSPNet), which distributes the computational load across  
Figure 2: YOLOv9 architecture diagram. The model demonstrates the YOLOv9 architecture’s processing flow for input images.  
different stages and network blocks, preventing excessive computation at any single stage and enabling flexible adjustment of computation based on hardware conditions. Dynamic resource adjustment allows GELAN to dynamically optimize the allocation of computational resources according to hardware conditions, ensuring high inference efficiency even when resources are limited. Finally, the modular design of the model, through the rational combination and integration of different network layers and modules, maintains high detection accuracy without adding excessive computational burden. The design of this architecture considers the balance between computational complexity and detection performance, ensuring that the model maintains high inference efficiency even with limited computational resources.  
The incorporation of these two innovative technologies is crucial for mitigating information loss during training while also optimizing efficiency and inference speed. These advancements strengthen the model's competitiveness and practical relevance in real-world situations.