# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 4 Experimental Results
In this case, the best models in terms of speed and model size are the original Yolov5-n model, the Yolov5-n model with the backbone structure modified by MobileNetV3s, and the Yolov8-n model, respectively. The number of layers used in the models, the number of parameters used (in millions), and the number of GFLOPs are listed in Table 5. Flops is a unit of measurement used to measure performance, reporting the number of floating-point calculations a processor performs, and GFLOPs is a unit of measurement equal to one billion flops. The complexity of the models was measured in GFLOPs, and the parameters represent the size of the model. The smaller the parameters and GFLOPs, the less processing power the model needs to use, and the model size will also be reduced. The model with the smallest parameters and GFLOP values, that is, requiring the least processing power, was the original Yolov5-n model. However, because even a small error is of vital importance in detecting an important situation, such as a forest fire, the priority here is to have a high accuracy rate for the model. Therefore, the proposed model, which has the highest mAP value, was recommended. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Backbone** & **Training Time (h)** & **Model Size (MB)** \\ \hline Yolov8-n & Yolov8 & 0.22 & 6.2 \\ Yolov8-s & Yolov8 & 0.36 & 22.5 \\ Yolov8-m & Yolov8 & 0.72 & 52.0 \\ Yolov8-l & Yolov8 & 1.08 & 87.6 \\ Yolov8-x & Yolov8 & 1.75 & 136.7 \\ \hline Proposed Model & & 1.22 & 138.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Backbone** & **Layers** & **Parameters (M)** & **GFLOPs** \\ \hline Yolov5-n & Yolov5 & 157 & 1.7 & 4.1 \\ Yolov5-n & EfficientNet\_b1 & 513 & 6.5 & 11.7 \\ Yolov5-n & MobileNetV3s & 287 & 2.1 & 3.1 \\ Yolov5-n & ResNet34 & 197 & 22.4 & 61.9 \\ Yolov5-n & ResNet50 & 232 & 27.5 & 72.1 \\ \hline Yolov5-s & Yolov5 & 157 & 7.0 & 15.8 \\ Yolov5-s & EfficientNet\_b1 & 513 & 9.4 & 16.2 \\ Yolov5-s & MobileNetV3s & 287 & 4.6 & 7.2 \\ Yolov5-s & ResNet34 & 197 & 24.9 & 66.2 \\ Yolov5-s & ResNet50 & 232 & 30.9 & 77.7 \\ \hline Yolov5-m & Yolov5 & 212 & 20.8 & 47.9 \\ Yolov5-m & EfficientNet\_b1 & 533 & 16.0 & 27.9 \\ Yolov5-m & MobileNetV3s & 307 & 10.8 & 18.7 \\ Yolov5-m & ResNet34 & 217 & 31.2 & 77.8 \\ Yolov5-m & ResNet50 & 252 & 38.1 & 90.5 \\ \hline Yolov5-1 & Yolov5 & 267 & 46.1 & 107.7 \\ Yolov5-1 & EfficientNet\_b1 & 553 & 27.7 & 49.6 \\ Yolov5-1 & MobileNetV3s & 327 & 22.1 & 40.0 \\ Yolov5-1 & ResNet34 & 237 & 42.5 & 99.3 \\ Yolov5-1 & ResNet50 & 272 & 50.3 & 113.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Layers, parameters, and GFLOPs information for the models. The PR curves of the test results of the proposed model, the original Yolov5-x model, and other Yolov5-x models created by changing the backbone structures are shown in Figure 6. The model that yields the highest mAP value of 98% in the smoke class is the proposed model. With mAP values of 96.3% and 96%, the two best-performing models in the smoke class after the proposed model were the Yolov5-x and Yolov5-x models, whose backbone structures were modified with MobileNetV3s. The highest mAP value of the normal class was the Yolov5-x model, whose backbone structure had been modified with MobileNetV3s (96%), which was compared to the proposed model and the original model, with mAP values of 95.9% and 92.6%, respectively. When Figure 6 is examined, the model that gives the highest mAP value of 98% in the smoke class is the proposed model (Figure 6e). With mAP values of 96.3% and 96%, the two best-performing models in the smoke class after the proposed model are the Yolov5-x model (Figure 6a) and the Yolov5-x model whose backbone structure had been modified with MobileNetV3s (Figure 6c). The graphs given in Figure 5 allow us to comment on the performance of the model depending on the size of the area under the curve. Model performance is directly proportional to the size of the area under the curve.