## 3 Textual Method  
The algorithm framework in this paper is divided into four system parts, namely Backbone, Neck, Head, and Loss. As shown in Figure 2, the Neck layer extracts the DDU structure of small target features, and the optimized attention mechanism PNCO module of feature fusion is the innovation point of this paper. The following part will briefly introduce the Backbone and Head layer and then focus on the Neck layer optimized in this paper as follows:The network mechanism proposed in this method is based on the universality and real-time consideration of engineering applications, so the thinking of first-order object detection has been adopted. Therefore, a new deep learning network structure for target recognition has been proposed based on YOLOv8 [30; 31]. The Backbone of this network uses the CSP (CrossStagePartial) framework. The existence of this framework not only improves the learning ability of the CNN but also reduces the calculation cost of the model to a certain extent. At the same time, different from other combinations of three convmodules and n DarkNets, its network is composed of two convmodules and n Darknets and connected through Split and Concat. Meanwhile, the ConvModule here is composed of Convn-Bn-Relu. Compared to the ConvnModule composed of Convn-Relu-Bn, the structure adopted in this paper is more reasonable. For the detection head of this network, the decoupled head was selected, which is embodied in that the classification and detection head are separate. For the loss function, positive and negative samples were allocated. We used the Task-Aligned One-Stage Object Detection Task-Aligned Assigner in the training model to select positive samples according to the weighted scores of classification and regression. The formula is shown as follows:  
\[t=s^{\alpha}\times u^{\beta} \tag{2}\]  
The \(s\) represents the prediction of tags in the class big score. The \(u\) represents the prediction of the IoU and the ground truth bounding box. In addition, our model classification uses binary crossentropy, whose equation is as follows:  
\[Loss_{n}=-w[y_{n}\log x_{n}+(1-y_{n})\log(1-x_{n})] \tag{3}\]  
where \(w\) is the weight, \(y_{n}\) is the annotation value, and \(x_{n}\) is the model prediction value. The regression branch adopts the distributed Focal Loss \(DFL\) and the Complete IoU _CloU_, where the \(DFL\) is used to expand the probability around the object \(y\), and the specific equation is as follows:  
\[DFL(\varphi_{n},\varphi_{n+1})=-((y_{n+1}y)\log(\varphi_{n})+(y-y_{n-1})\log( \varphi_{n+1})) \tag{4}\]  
Figure 2: The algorithm framework diagram of this article. The formulas for \(\varphi_{n}\) and \(\varphi_{n+1}\) are shown below:  
\[\varphi_{n}=\frac{y_{n+1}-y}{y_{n+1}-y_{n}}\quad\varphi_{n+1}=\frac{y-y_{n}}{y_{ n+1}-y_{n}} \tag{5}\]  
The \(CloU\) and \(DloU\) involved here add an impact factor on the basis of Loss while considering the predicted aspect ratio and ground truth boundary box, and the specific equation is as follows:  
\[CloU_{Loss}=1-IoU+\frac{Distance_{2}^{2}}{Distance_{C}^{2}}+\frac{v^{2}}{(1- IoU)+v} \tag{6}\]  
where \(v\) is the parameter to measure the consistency of the aspect ratio, which is defined as follows:  
\[v=\frac{4}{\pi^{2}}\bigg{(}\arctan\frac{w^{\text{gt}}}{h^{\text{gt}}}-\arctan \frac{w^{\text{p}}}{h^{\text{p}}}\bigg{)}^{2} \tag{7}\]  
where \(w\) is the weight of the bounding box, and \(h\) is the height of the bounding box. For the network at the Neck layer, generally speaking, the deeper the network is, the more feature information it obtains. However, the deeper network will reduce the position information of objects, and too much convolutional behavior will lead to the loss of large information of small objects. Therefore, it is crucial to merge the necessary feature pyramid with the features that enhance the small target information and classify them into target features. In this model, the FPN is used for upsampling from top to bottom.