# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Algorithms  
DL-based algorithms are classified in two ways: two- and one-stage detectors. Algorithms called two-stage detectors first use a network to determine the regions where objects are located in the image, and then use a second structure to precisely detect objects in these regions [17]. The long operating time of the two-stage detectors led to the creation of the Yolo alternative one-stage detector model [18]. Yolo, which performs the location and classification of the object in one go by passing the image with a smaller model size over a single network, is not only faster, but also offers real-time detection [19]. Yolov1, introduced in 2016, significantly improved both detection speed and accuracy compared to earlier approaches [20]. NMS is performed based on the determined confidence scores, and NMS ensures that only a single bounding box is drawn by taking the box with the highest confidence score among the bounding boxes drawn for an object [21]. Yolo consists of a structure containing 24 convolutional layers, 2 fully connected layers to detect objects, and 1 \(\times\) 1 convolutional layers to increase speed by reducing the number of parameters. Although Yolov1 is a good and fast object detection model, its usefulness in detectingobjects of different image sizes that are not in the training data is insufficient; it detects a maximum of two objects belonging to the same class for each grid, and it has limitations, such as more localization errors compared to the other models [22]. Yolov2 [23] is a new and updated object detection model developed to overcome the limitations of the previous version, Yolov1. The limitations of Yolo models are as follows: although they increase the acceleration by reducing the number of calculations, there is no good improvement in terms of the mAP value, the simplicity of the CNN network, and the fact that it does not use a structure such as RPN, which causes the accuracy value to remain low compared to other models, and Yolo fails to detect small objects successfully. A long time after the launch of Yolov3 [24], the 4 th version of the Yolov4 [25] series was introduced in 2020. The model basically consists of three structures: the backbone, which enables the extraction of features; the neck part, which develops the features to increase the usability of the extracted features; and the head, which is responsible for the prediction process. Yolov4 uses CSPDarknet53 as the backbone. CSP structures were used to reduce the computational effort while maintaining the accuracy of model determination. Mish [26], which has self-organizing ability, was used in the activation function selection. In the feature collection process, PANet is combined with FPN, which goes from top to bottom and has an opposite bottom-up structure; however, unlike the original structure of PANet, a PANet structure that combines features instead of collecting them is used [27]. Despite the innovations made in Yolov4, this is insufficient for detecting small objects owing to its high-resolution prediction structure and the use of a large anchor box. PyTorch was used as a framework to abandon Darknet in the Yolov5 version, [28]. A modified CSPDarknet53 was used in the backbone structure to increase the speed and accuracy and reduce the number of parameters and calculations. The focus layer, first introduced in Yolov5, was used in the backbone layer to reduce the CUDA memory used and increase the forward and backward propagation [29]. After the focus layer, convolutional layers were used as the feature extractors. The BN and SiLU activation functions were added to each convolutional layer. The pooling layer used in the backbone structure, namely SPP, performs the function of eliminating the size limitation by combining the features of different scales in a specific feature map. PANet created a neck structure to increase the importance of low size features and the accuracy rate in the localization process.