# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Running the Models  
The model developed in this study was compared not only with the classical architectural structures of Yolo models, but also with the performances of different hybrid models. In this context, five models of Yolov5 (n-s-m-l-x), five models of Yolov8 (n-s-m-l-x), and twenty models whose backbone structures were modified with EfficientNet_b1, MobileNetV3s, ResNet34, and ResNet50 were used in this study. In this process, 30 object detection models were tested using Google Colab, and the results were examined.  
In total, 737 image data belonging to the Wildfire Smoke Dataset, as well as 268 image data containing smoke-like objects taken from open-source websites, were processed on the Colab platform using the Python programming language and libraries such as Keras, Pytorch, and Tenserflow. All experiments were conducted using Python 3.13 with the PyTorch deep learning framework, running on a workstation equipped with an NVIDIA, SantaClara, ABD, RTX 3080 GPU, Intel Core i7-12700K CPU, and 64 GB Ram. From a total of 1005 image data points belonging to our advanced 2-class dataset, 805 images (80%) were used for training, 100 images for validation (10%), and the remaining 100 images (10%) were used for testing.  
The most appropriate parameter values of the DL models for forest fire detection were determined and consistently applied across all algorithms to ensure a fair comparison. These values included a learning rate of 0.01, a momentum of 0.937, the SiLU activation function, the SGD optimization algorithm, and 100 training epochs across all backbone architectures and model variants.  
The epoch, which is the number of times the image data in the dataset are displayed to the network with label information, was determined to be 100 for all models. The optimization algorithm is a structure that increases the performance in complex learning processes in deep learning models and is designated as SGD in all models. The momentum value used to ensure fast SGD operation was determined to be 0.937 in all models. In deep learning, weight values are updated by finding the difference by taking the derivative during the backpropagation process, multiplying this difference with the learning rate value and subtracting the result from the old weight values to find new weight values. The learning rate value for all models was determined to be 0.01. The activation function, which was used to convert the results to non-linear values, was used to improve the performance. A designated SiLu activation function was used for all the models. This was preferred over Relu to reduce the problem of Gradient Vanishing and to obtain results that were more sensitive to negative inputs.  
\[S\textit{ILU}(\alpha)=\alpha\cdot\sigma(\alpha) \tag{3}\]  
In the formulation, \(\alpha\) gives the input value while \(\sigma(\alpha)\) defines the sigmoid function. The formula of the sigmoid function is as follows. The output range is shown between 0 and 1. It is used especially in the last layer in two-class models.  
\[\sigma(\alpha)=\frac{1}{1+e^{-x}} \tag{4}\]