# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 4 Experimental Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Models** & **Backbone** & **Precision** & **Recall** & **F1** & **mAP** \\ \hline Yolov5-n & Yolov5 & 0.955 & 0.953 & 0.953 & 0.947 \\ Yolov5-n & EfficientNet b1 & 0.940 & 0.865 & 0.900 & 0.934 \\ Yolov5-n & MobileNetV3s & 0.917 & 0.879 & 0.897 & 0.924 \\ Yolov5-n & ResNet34 & 0.844 & 0.881 & 0.862 & 0.913 \\ Yolov5-n & ResNet50 & 0.945 & 0.959 & 0.951 & 0.960 \\ \hline Yolov5-s & Yolov5 & 0.895 & 0.906 & 0.900 & 0.910 \\ Yolov5-s & EfficientNet b1 & 0.838 & 0.873 & 0.855 & 0.930 \\ Yolov5-s & MobileNetV3s & 0.945 & 0.920 & 0.932 & 0.942 \\ Yolov5-s & ResNet34 & 0.784 & 0.840 & 0.811 & 0.878 \\ Yolov5-s & ResNet50 & 0.919 & 0.959 & 0.938 & 0.965 \\ \hline Yolov5-m & Yolov5 & 0.926 & 0.953 & 0.939 & 0.960 \\ Yolov5-m & EfficientNet b1 & 0.699 & 0.785 & 0.739 & 0.820 \\ Yolov5-m & MobileNetV3s & 0.913 & 0.939 & 0.925 & 0.939 \\ Yolov5-m & ResNet34 & 0.817 & 0.897 & 0.855 & 0.907 \\ Yolov5-m & ResNet50 & 0.939 & 0.967 & 0.952 & 0.956 \\ \hline Yolov5-l & Yolov5 & 0.939 & 0.940 & 0.939 & 0.945 \\ Yolov5-l & EfficientNet b1 & 0.611 & 0.852 & 0.711 & 0.797 \\ Yolov5-l & MobileNetV3s & 0.940 & 0.959 & 0.949 & 0.956 \\ Yolov5-l & ResNet34 & 0.791 & 0.884 & 0.834 & 0.905 \\ Yolov5-l & ResNet50 & 0.937 & 0.928 & 0.932 & 0.960 \\ \hline Yolov5-x & Yolov5 & 0.895 & 0.940 & 0.916 & 0.945 \\ Yolov5-x & EfficientNet b1 & 0.845 & 0.929 & 0.884 & 0.936 \\ Yolov5-x & MobileNetV3s & 0.950 & 0.944 & 0.946 & 0.960 \\ Yolov5-x & ResNet34 & 0.864 & 0.919 & 0.890 & 0.922 \\ \hline Yolov8-n & Yolov8 & 0.884 & 0.918 & 0.900 & 0.942 \\ Yolov8-s & Yolov8 & 0.856 & 0.927 & 0.890 & 0.926 \\ Yolov8-m & Yolov8 & 0.825 & 0.908 & 0.864 & 0.897 \\ Yolov8-l & Yolov8 & 0.821 & 0.850 & 0.835 & 0.865 \\ Yolov8-x & Yolov8 & 0.844 & 0.824 & 0.833 & 0.856 \\ \hline Proposed & & & & & \\ Model & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model test results. To evaluate the performance of the models and measure their usability in a real application environment, it is necessary to examine the training time and model size values. In Table 4, the training time and model size values of the models are presented. The fastest model here is the original Yolov5-n model with a 0.21 h training time. In terms of speed, the Yolov5-n model is followed by the Yolov5-n and Yolov8-n models, whose backbone structure has been replaced with MobileNetV3s, with training time values of 0.22 h. The smallest model in terms of size was the original Yolov5-n model with a size of 3.9 MB. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Backbone** & **Training Time (h)** & **Model Size (MB)** \\ \hline Yolov5-n & Yolov5 & 0.21 & 3.9 \\ Yolov5-n & EfficientNet\_b1 & 0.69 & 13.8 \\ Yolov5-n & MobileNetV3s & 0.22 & 4.7 \\ Yolov5-n & ResNet34 & 0.51 & 45.3 \\ Yolov5-n & ResNet50 & 0.79 & 55.6 \\ \hline Yolov5-s & Yolov5 & 0.30 & 14.4 \\ Yolov5-s & EfficientNet\_b1 & 0.73 & 19.5 \\ Yolov5-s & MobileNetV3s & 0.25 & 9.7 \\ Yolov5-s & ResNet34 & 0.53 & 50.3 \\ Yolov5-s & ResNet50 & 0.85 & 62.5 \\ \hline Yolov5-m & Yolov5 & 0.58 & 42.2 \\ Yolov5-m & EfficientNet\_b1 & 0.88 & 32.8 \\ Yolov5-m & MobileNetV3s & 0.31 & 22.3 \\ Yolov5-m & ResNet34 & 0.62 & 62.9 \\ Yolov5-m & ResNet50 & 0.95 & 76.8 \\ \hline Yolov5-l & Yolov5 & 0.97 & 92.9 \\ Yolov5-l & EfficientNet\_b1 & 0.95 & 56.2 \\ Yolov5-l & MobileNetV3s & 0.42 & 44.9 \\ Yolov5-l & ResNet34 & 0.73 & 85.5 \\ Yolov5-l & ResNet50 & 1.06 & 101.3 \\ \hline Yolov5-x & Yolov5 & 1.58 & 173.1 \\ Yolov5-x & EfficientNet\_b1 & 1.12 & 92.7 \\ Yolov5-x & MobileNetV3s & 0.63 & 80.7 \\ Yolov5-x & ResNet34 & 0.90 & 121.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model training time and model size. Figure 5: Model heatmap metrics. The Yolov5-n model is followed by the Yolov5-n model and the Yolov8-n model, with the backbone structure replaced by MobileNetV3s, with model sizes of 4.7 MB and 6.2 MB, respectively.