# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Proposed Model
BottleneckCSP (Cross-Stage Partial) structures are also used in the neck section. This structure contributes to achieving high accuracy with a lower parameter count and processing load compared to traditional convolutions. In the last stage, multi-scale feature maps obtained from the neck layer are processed for classification and location estimation via Conv2D layers. The model has 19.7% fewer parameters and 26.8% lower FLOPs compared to the original Yolo architecture. These improvements reduced the computational time by 22.7% and significantly reduced the model size. The multiplicity of the layers in the DL structure, which is used to solve computer vision and similar difficult and complex problems, expands the capacity. However, because small gradient values are multiplied during backpropagation, extremely small values are obtained in structures with many layers; therefore, the change in the weight values decreases to almost zero. The skip connection structure shown in Figure 4 was used as a solution to the ResNet vanishing gradient problem. The skip connection structure works by skipping the layers in a flat network, which degrades its performance. This structure forms the basis of ResNet, and is the residual block structure that gives its name to the model. The skip connection enabled faster propagation of inputs between the layers. Under normal circumstances, a bias term is added to the value obtained by multiplying the input (i.e., x) by the weight value in the layer and passing it through an activation function (i.e., F(x)) (see Equation (1)). \[H(x)=F(ux+b) \tag{1}\]  
However, the skip connection enabled the output (i.e., H(x)) to be calculated for the sum of the inputs and the inputs passed through the activation function (see Equation (2)). \[H(x)=F(x)+x \tag{2}\]  
In addition, increasing the number of layers in neural networks expands the working space of the model and results in too many functional possibilities, making it very difficult to reach the target function. Therefore, convergence was almost impossible. This situation increases the possibility of moving away from the target function when attempting to approach it in experimental practice. For this reason, the function areas are nested within the model architecture to obtain better results without reducing the model quality. With  
Figure 4: Skip connection structure. Figure 3: Proposed model. this method, an increase in the number of layers did not negatively affect the performance of the model. In short, the convergence problem was solved by nesting the functional areas. In addition, an increase in the number of functions increases the number of non-linear results. The increase in the non-linear results also leads to increased performance and provides better results for complex structures. In addition, by adding 1 \(\times\) 1 convolution to the bottleneck blocks used, the 3 \(\times\) 3 convolution was changed, and the number of parameters was reduced. To match the input and output sizes, the shortcut connection was size-matched using a 1 \(\times\) 1 convolution. Thus, by decreasing the parameters, the training time was also shortened.