# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 2 Materials and Methods
### Improved YOLO8 Model
#### 2.3.3 Detection with Integrated CA Attention Mechanism  
In the extraction of crucial features from forest fire images with complex backgrounds, YOLOv8 incorporates the Coordinate Attention module. The CA attention module, introduced by [PERSON] and colleagues, aims to capture extensive dependencies among image features [21]. Contrary to conventional attention mechanisms, Coordinate Attention selectively focuses on spatial positions pertinent to the task at hand. The diagram of CA is depicted in Figure 8.  
Figure 6: The structure of SCConv is built-in with the SRU and CRU. This diagram indicates the specific function of our SCConv module inside a ResBlock.  
Figure 7: The structure of the \(SCConv\_C2f\) module.  
CoordAtt embeds positional data into channel attention, cleverly fending off the introduction of 2D world pooling. Instead, it employs an approach of decomposing channel interest into two one-dimensional characteristic encodings. This approves the enter elements to be cleverly aggregated into two independently direction-aware function maps alongside the vertical and horizontal directions. These characteristic maps no longer solely comprise embedded directional facts but additionally seize long-distance spatial dependencies alongside the spatial path through the interest maps generated through encoding. Finally, these two interest maps are accelerated with the enter function map to emphasize the expression of the areas of interest.  
During the embedding of coordinate information, the challenge of global pooling is addressed by horizontally and vertically decomposing pooling. Specifically, for each feature output, it is represented as:  
\[z_{ch}(h)=\frac{1}{W}\sum_{i=0}^{W-1}x_{c}(h,i),\]  
\[z_{cw}(w)=\frac{1}{H}\sum_{j=0}^{H-1}x_{c}(j,w),\]  
where \(H\) and \(W\) denote the height and width of the pooling kernel, respectively. These operations combine elements across two spatial orientations, generating a set of direction-aware feature maps that capture spatial dependencies while preserving positional information concurrently. The technology of Coordinate Attention entails concatenation observed by means of subsequent \(1\times 1\) convolution. The vertical and horizontal spatial records are encoded via BatchNorm and non-linear activation. This encoded data, segmented, undergoes every other \(1\times 1\) convolution to alter the channel measurement of the interest map to align with the input. The whole method concludes with normalization and weighted fusion by use of the sigmoid function, expressed as:  
\[y_{c}(i,j)=x_{c}(i,j)\cdot g_{c}^{h}(i)\cdot g_{c}^{w}(j),\]  
where \(x_{c}(i,j)\) is the input feature map, and where \(g_{c}^{h}(i)\) and \(g_{c}^{w}(j)\) are the attention weights in the two spatial directions.  
The integration of the Channel Attention module addresses the challenge of incomplete feature extraction arising from significant variations in forest fire images. The CA module enables the convolutional network to concentrate on learning crucial regions within forest fire elements, thereby comprehending the entire image more effectively by preserving long-range dependency information. The seamless fusion of the CA module with YOLOv8's backbone network successfully extends the receptive field, extracting more comprehensive positional information. This enhancement enables the model to adeptly adapt to multi-scale forest fire images.  
Figure 8: The schematic diagram of CoordAtt.