# YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
## 2 Materials and Methods
### Identification Method
#### 2.2.1 YOLOv8 Model  
The YOLOv8 (You Only Look Once version 8) target detection model, released by the Ultralytics team in January 2023, was the state-of-the-art (SOTA) algorithm in the field of target detection for that year [27; 28]. The YOLOv8 model maintains the classic framework of its predecessors, consisting of three main components: the Backbone network for feature extraction, the Neck network for feature fusion, and the Head for classification and localization. The three-part structure of YOLOv8 is shown in Figure 6.  
The Backbone component of YOLOv8 builds on the CSP (Cross Stage Partial) design concept and incorporates elements from the ELAN (Efficient Layer Aggregation Network) used in the C3 module of YOLOv5 [29]. The design was simplified by removing a Conv module to create a residual connection, transforming C3 into C2. Additionally, the introduction of more skip connections through the Split operation helped reduce model parameters, enhance gradient flow, and mitigate gradient disappearance. This modification led to faster processing speeds and was named the C2f module. This adjustment not only represents the most significant enhancement in the YOLOv8 network structure but also serves as a key point of optimization in this paper. Figure 7 shows the C2f structure. The structure of the Neck component was relatively complex. The SPPF (Spatial Pyramid Pooling Fast) module, which connects the upper and lower parts, follows the  
Figure 6: YOLOv8 network structure diagram. Figure 7: C2f structure diagram. design principles of YOLOv5 to enhance the receptive field range of the feature map and improve feature separation [30]. Feature fusion is achieved through a PAN-FPN (Path Aggregation Network for Feature Pyramid Networks) structure. However, unlike the C2f module in the Backbone, the Bottleneck structure of the C2f module in the Neck network does not employ residual connections. Figure 8 shows the SPPF layer, and the Neck structure can be found in Figure 6.  
The Head section is relatively simple, with each of the three scales having its own detector. Each detector consists of two branch paths: one for classification tasks and the other for localization tasks. This structure is known as a Decoupled-Head. During training, the results from each path are sent directly to their respective loss function algorithms to calculate loss and update the network through iteration. During inference, the six results are first concatenated and then split into regression task results and classification task results from different dimensions. It is worth noting that YOLOv8 employs an Anchor-based mechanism during training but switches to an Anchor-free mechanism during inference, with the final output being the two key points (Anchor points) at the upper left and lower right of the target. The structure of the detection Head is shown in Figure 9.  
In addition to the above changes in the network structure, improvements have been made in label allocation, loss function, etc. YOLOv8 adopted a dynamic label allocation strategy, which combined the methods of Optimal Transport Assignment (OTA) and Task-aligned One-stage Object Detection (TOOD) during training to implement the Task-Aligned Assigner to improve allocation efficiency and quality [31; 32]. The decoupling Head of YOLOv8 had two branches, each using different loss functions. The classification branch maintained the Binary Cross-entropy Loss function (BCE), and the regression branch combined the Complete Intersection over Union loss function (CIoU loss) and the distribution focal loss function to calculate together.