## 3 Methodology
### Implementation Details  
#### 3.2.1 The Minimalist Journey of C2f  
YOLOv8 is a cutting-edge SOTA model based on the YOLO architecture, introducing new features and enhancements for an improved performance and flexibility. One of the most important innovations in YOLOv8 is the use of the C2f module. However, due to the complexity of its core modules, the large number of parameters, and the size of the model, YOLOv8 still presents challenges in terms of deployment. Inspired by VanillaNet [60], we adopted VanillaBlock to replace the bottleneck in the C2f module as the main gradient flow branch and created a new C2f_Van module. This mechanism not only ensures a lightweight model but also obtains richer gradient flow information. VanillaNet, proposed by researchers at Huawei Noah's Ark Laboratory and the University of Sydney, is a neural network model that embodies the idea of minimalism. The model contains only the most basic convolution calculations, eliminates residuals and attention modules, and solves the complexity problem, making it well suited for resource-constrained environments. However, the simplicity and relative shallowness of this architectural framework, as well as its weak nonlinearity, limit its performance. Therefore, to enhance the nonlinearity of the VanillaNet structure, a deep training strategy and a series of informed activation functions are proposed to solve this problem. The essence of the deep training strategy is to decompose a convolutional layer into two convolutional layers during the training process and insert the following nonlinear operations in between. The operation process is shown in Equation (1):  
\[A^{\prime}(x)=(1-\lambda)A(x)+\lambda x \tag{1}\]  
Here, \(A(x)\) represents a traditional nonlinear activation function (which can be common functions such as the LeakyReLU and ReLU), and \(\lambda\) is a hyper-parameter used to balance the nonlinearity of the modified activation function (\(A^{\prime}(x)\)). It gradually tends towards \(1\) during model optimization, allowing the two convolutional layers to be merged into one layer and reducing the inference time. In the activation function informed by the series, the concurrent stacking of the activation functions is used to enhance the nonlinearity of the activation layers. If a single activation function for input \(x\) in a neural network is denoted as \(A(x)\), then the concurrent stacking of \(A_{s}(x)\) can be represented as Equation (2):  
\[A_{s}(x)=\sum_{i=1}^{n}a_{i}A(x+b_{i}) \tag{2}\]  
In Equation (2), \(A_{s}(x)\) represents a series-based activation function, \(n\) represents the number of stacked activation functions, and \(a_{i}\) and \(b_{i}\) are the scale and bias for each activation, to avoid simple accumulation. Concurrent stacking significantly enhances the nonlinearity of the activation function, improving the ability to perceive information. The design of the C2f_Van module is illustrated in Figure 3. Initially, two slices are used to partition the output of the convolution into two parts, aiding in more flexible feature handling. In the VanillaBlock module, by using two \(1\times 1\) standard convolutions followed by a nonlinear function, the number of channels is reduced, and the parameters and model calculation are reduced effectively. Experiments indicate that this design can enhance the expressiveness of the model. In addition, \(7\times 7\) convolution is used at the end of the model structure, and the acceptance domain of the model is extended. This mechanism enhances the ability of the model to acquire global features and significantly improves the detection performance. A cross-layer feature fusion strategy and truncated gradient flow technique are used in the C2f_Val structure to enhance the variability in the learning features between the layers of the network. The advantages of this mechanism are the reduction in the influence of redundant gradient information and the enhancement of the learning ability of the network.