# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Proposed Model  
Although Yolo models bring different features to each newly developed system, object detection systems have not been developed at the desired level. In this context, the model was developed using the hyperparameters of the most stable Yolov5 model. Although the Yolov5 algorithm is a popular model for object detection, it requires some parameter changes in the model to increase the detection accuracy and improve its performance. When detecting an object, the target-object-specific features obtained from the image data are the most important factors in detecting this region. Therefore, to develop the Yolov5 object detection model, we focused on the backbone structure, which is a part of the model that performs feature extraction and creates feature maps at different resolutions. Instead of Yolov5's backbone structure, a new network was developed by integrating the ResNet50 [40] model. The architectural structure of model is illustrated in Figure 2.  
In the original ResNet50 structure, the stride value is 2 in a 1 \(\times\) 1 convolution in bottleneck blocks that require subsampling. However, the stride value was set to 2 in the  
Figure 2: Structure of model. 3 \(\times\) 3 convolution. The stride value was taken into a 3 \(\times\) 3 convolution to increase the accuracy rate. In the first layer of the model, a convolution operation of 7 \(\times\) 7 in size and 64 filters are applied, followed by a 3 \(\times\) 3 max pooling layer to reduce the spatial dimension. In this way, the initial stage allows the basic patterns to be captured in a wide area. To use ResNet50 in the backbone as a feature extractor, the average pooling layer and classifier softmax layer were removed. After these changes, the original Yolov5 backbone structure was replaced with the developed ResNet50-based backbone model. Residual blocks were used as the basic building blocks of the model. In addition, the residual connections in each block enable the network to become deeper and provide resistance to the vanishing gradient problem. Each residual block, after processing the input, was collected with the input via a direct shortcut link to form the model. The residual block basically learns the difference between an input (x) and the learned transformation F(x). This block contains a \"shortcut link\" that connects the input directly to the output. The global average pooling layer in the last part of the network summarizes the feature maps, then reaches an output with 100 classes through the fully connected layer, and finally the classification process is performed with the softmax activation function. The architecture of the hybrid model is shown in Figure 3. In this proposed model, a ResNet-based model structure integrated into the Yolo architecture is presented. This structure offers a more optimized method in terms of feature fusion and information transfer compared to the classical Yolo architecture. In particular, the CSP structures in the neck section were developed with the aim of increasing the accuracy while reducing the computational load of the model. There are three basic sections in the architecture: the backbone, neck, and output. This structure is optimized for high accuracy and low latency object detection. In the backbone section, the image given to the input of the model is first passed through a convolution layer of 7 \(\times\) 7 size and 64 filters, then a 3 \(\times\) 3 maximum pooling process is applied to provide dimensionality reduction. This stage is followed by bottleneck blocks repeated 3, 4, 6, and 3 times, respectively. Each block consists of convolution layers of 1 \(\times\) 1, 3 \(\times\) 3 and again 1 \(\times\) 1 sizes, and residual connections minimize information loss and gradient fading problems in the deep learning process. This structure allows for the efficient extraction of multi-layer features from low to high levels. Multi-scale feature maps obtained from the backbone are enriched in the neck section and made more meaningful.