# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Algorithms
Although the head structure is the same as in the previous version, it is designed to provide three different feature maps as outputs to increase the efficiency for detecting small and large objects. The details of the architectural structure of Yolov5 are given in Figure 1. Another notable innovation is the use of the AutoAnchor structure, which allows for the creation of anchor boxes of similar sizes based on real object boundaries in the training image data in the dataset via k-means [27]. The makers of the Yolov5 algorithm presented five models with different width and depth dimensions for the convolutional structures. Nano (n) and small (s) models are lightweight models designed to operate with low system requirements. Large (l) and extra-large (x) models are heavy models designed to achieve high performance and are used in more powerful devices, although they cause slow speeds. The medium (m) model is designed to work in a more balanced way in terms of performance and speed compared to other models. In addition, the anchor used in the other models was not used in this model. Just a month after the release of the Yolov6 [30], Yolov7 [31] was launched as a new model in July 2022. First, it was used in the E-ELAN network backbone structure, which combines the features of different classes without disturbing the original gradient path by checking the shortest and longest gradient paths, taking into account the factors affecting accuracy and speed [32]. In addition, compound model scaling was introduced by consistently changingthe width and depth of the network to meet various application requirements while maintaining the optimal network structure. Models of various sizes were produced using compound model scaling. Inspired by the RepConv structure, a RepConvN convolutional layer without an identity connection was used [33]. A different approach is presented for the head, which is responsible for object detection. In this context, there is an auxiliary head in the middle layer that increases the learning ability by updating the weights during the training process. The label assigner calculates predictions and real situations, and transmits the flexible labels it creates to the lead head structure responsible for the final result output [34]. Yolov6 is a version of Yolo that allows for integrated use in IoT and embedded systems [35]. Therefore, community support may be more limited compared to other versions, such as Yolov5 or Yolov8. There may be uncertainty about model updates and long-term maintenance. In January 2023, the latest and most updated version of Yolo, Yolov8 [36], was made available by Ultralytics, London, United Kingdom with five different-scale models. Nano (n) and small (s) models, which are different-scale models designed by changing depth, width, and ratio values, work fast and have reduced parameter values and calculation processes, but have lower accuracy performance than other models. These two models are lightweight and designed for use in systems that do not require many hardware features. Large (l) and extra-large (x) models have high accuracy but are slower than lightweight models because of their high parameter values. The Medium (m) model was designed to provide a more balanced performance between lightweight and large-scale models in terms of both performance and speed. The application area of Yolov8 has been expanded by introducing different scale models to perform small and large object detection. In object detection algorithms that use anchors, if the aligned anchors are not correctly positioned, they cause errors. This is why an anchor is not used in Yolov8. However, it uses task alignment learning [37]. C2f and CBS modules are used in the backbone structure, which increases the performance and makes feature extraction more efficient. In addition, SPPF is used, which extracts various features using three pooling processes at the end of the backbone section, expands the receptive field of the network, and thus enables more accurate object detection in complex images [38].