## 3 DCNA-YOLO Method Construction  
### Multi-Branch Coordinate Attention  
Critical information about objects may be obscured by noise, image backgrounds, and uneven lighting due to complex, blurry, and poorly lit environments. Therefore, enhancing the positional information of features is a significant challenge. To address this issue, attention mechanisms [28] have been introduced in recent years. Among these methods, the coordinate attention mechanism (CAM) [29] effectively enhances the extraction of structural information about objects by using two one-dimensional average pooling operations to aggregate the feature maps vertically and horizontally into two separate orientation-aware feature maps, which are subsequently encoded into an attention tensor containing orientation-position information, and ultimately decomposed into a pair of attention maps that are both orientation- and position-aware. Although the CAM is effective in capturing long-range dependencies in local spatial information, it overlooks the global dependencies necessary for understanding spatial features. To address this limitation, a global context perception module is introduced into the CAM, aiming to help the network acquire global information by considering the overall context comprehensively. This results in more precise and comprehensive image representations for processing tasks. Additionally, by optimizing attention weights for direction awareness, position sensitivity, and global perception, the network can selectively focus on key areas of the target, significantly improving coordinate localization accuracy. As shown in Figure 3, the multi-branch coordinate attention (MBCA) principle is described, which consists of two steps:  
Step 1: In the information embedding phase, each channel of the input feature map X is encoded using two spatial range pooling kernels: (\(H\), 1) and (1, \(W\)) to embed coordinate information. The kernels operate along the horizontal (width \(W\)) and vertical (height \(H\)) coordinates, respectively. Figure 2: Network structure diagram of YOLOv8s. \[ \begin{cases}z_{c}^{h}(h)=\frac{1}{W}\underset{0\leq i<W}{\sum}x_{c}(h,i)\\ z_{c}^{w}(w)=\frac{1}{H}\underset{0\leq j<h}{\sum}x_{c}(j,w)\end{cases}  \tag{1}\]  
where \(h\) and \(w\) represent the height and width of the current input feature map, respectively. \(c\) denotes the current input feature map's channel. \(z_{c}^{h}(h)\) denotes the output of channel \(c\) at height h, and \(z_{c}^{w}(w)\) denotes the output of channel \(c\) at width \(w\). The global information is additionally embedded in the CAM by encoding each channel of the input feature map X through global average pooling (GAP). \[Z_{c}=\frac{1}{h\times w}\underset{i=1}{\sum}\underset{j=1}{\sum}\underset{r} {\sum}x_{c}(i,j) \tag{2}\]  
where \(z_{c}\) represents the global information output of channel c.  
Step 2: At the coordinated and global attention generation phase, the aggregated feature maps \(Z^{h}\) and \(Z^{w}\) generated from Equation (1) are firstly concatenated, and then a 3 \(\times\) 1 convolution operation is applied for information fusion, compressing feature channels. After batch normalization and non-linear activation functions, the intermediate feature map is split along the spatial dimension into two independent tensors \(f^{h}\) and \(f^{w}\). Subsequently, two 1 \(\times\) 1 convolutions \(F_{h}\) and \(F_{w}\) are used to transform \(f^{h}\) and \(f^{w}\) into tensors with the same number of channels as the input X. Furthermore, to fully utilize the expressive representation of the aggregated feature maps and accurately highlight the regions of interest, a feature map optimization module is proposed. This involves passing the intermediate feature map f through a 1 \(\times\) 1 convolutional transformation function F1 to adjust the number of channels. After applying a non-linear activation function and a sigmoid function, a feature map optimization weight matrix \(g_{1}\) is obtained.