# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 4 Experimental Results
It can be seen here that the model with the largest area under the curve is the proposed model. We can understand this from the fact that the mAP value of all classes is the highest at 96.9%. The other two curves in Figure 6 belong to the Yolov5-x model whose backbone structure had been replaced with EfficientNet_b1 (Figure 6b) and the Yolov5-x model whose backbone structure had been replaced with ResNet34 (Figure 6d), respectively. Comparison of the Proposed Model and the Original Model  
It has been observed that the proposed model, which was developed by changing the backbone structure, outperforms the original Yolov5-x model and has the highest mAP value among the hybrid 30 shared models. With the changes made, the number of GFLOPs in the proposed model was reduced by 26.8%, and the number of parameters was reduced by 19.7% compared to the original model, creating a faster and more accurate model with less processing power. Although the number of layers in the original Yolov5-x model was 322, the number of layers was reduced to 292 in the proposed model. By changing these values, the training time of the proposed model was reduced by 22.7% and the model size by 19.7%, compared with the original model, resulting in a faster model. \begin{table}
\begin{tabular}{l l l l l} \hline
**Models** & **Backbone** & **Layers** & **Parameters (M)** & **GFLOPs** \\ \hline Yolov5-x & Yolov5 & 322 & 86.1 & 203.8 \\ Yolov5-x & EfficientNet\_b1 & 573 & 45.9 & 84.4 \\ Yolov5-x & MobileNetV3s & 347 & 40.0 & 74.5 \\ Yolov5-x & ResNet34 & 257 & 60.3 & 133.8 \\ \hline Yolov8-n & Yolov8 & 168 & 3.0 & 8.7 \\ Yolov8-s & Yolov8 & 168 & 11.1 & 28.6 \\ Yolov8-m & Yolov8 & 218 & 25.8 & 78.9 \\ Yolov8-1 & Yolov8 & 268 & 43.6 & 165.2 \\ Yolov8-x & Yolov8 & 268 & 68.1 & 257.8 \\ \hline Proposed & & & & \\ Model & & 292 & 69.1 & 149.1 \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._Although the precision value of the original Yolov5-x model as a result of the testing process was 89.5%, this value increased by 5.9% to 95.4% with the improved model. The recall value increased by 2.4%, and the F1 value by 4.2%, compared to the original model, reaching 96.4% and 95.8%, respectively. The mAP value, which is the most important value in interpreting and increased by 2.4-96.9%, making it the best performing model among the 30 models. Figure 7 is a violin graph showing the distribution of performance values for different metrics (precision, recall, F1, mAP). Each violin visualizes the density and distribution of values for a metric. For the precision metric, the values are mostly concentrated between 0.85 and 1.0. However, there are a few low precision values around 0.6 in the lower region (these low precision values were also noticeable in the previous heatmap). The recall values  
Figure 6: PR curves of proposed model. are slightly more narrowly distributed compared to those of precision, meaning there are no major differences in recall performance between models. The F1 score distribution is concentrated between 0.8 and 1.0. The distribution is wider than for precision and recall, indicating some differences in F1 score between the models. The mAP value is quite narrowly concentrated, mostly between 0.9 and 1.0. This shows that most models have consistent and high performance in terms of mAP. The effects of the operations performed on the model predictions are shown in Figure 8. Labeled image data (Figure 8a) are provided in the dataset. The label information in the labeled image data contains the actual boundaries that must be estimated. The original Yolov5-x model made some incorrect predictions (Figure 8b), as can be seen. These errors were caused by the model perceiving a smoke image as if there were two smoke images. As a result of the improvements made to the model, it can be seen that the improved model becomes more stable (Figure 8c). Figure 8: Performance comparison of models. (**a**) Original image (**b**) Yolov5-x (**c**) proposed model. Figure 7: Violin plot—metric distributions.