## 3 Experimental Results
### Results  
In the learning stage, NVIDIA's DGX A100 was used as a learning environment, as shown in Figure 19, and the Object Detection models provided by NVIDIA TAO, DetectNet_v2, FastRCNN, YOLOv4, EfficientDet, and DINO were developed and trained. The results of the evaluation using the test set of all learning models are shown in Table 2 below. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Virtual Data** & **Real-World Data** & **Total** \\ \hline Training data & 4375 & 412 & 4787 \\ Testing data & 625 & 90 & 715 \\ Total & 5000 & 502 & 5502 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of training images for a single specific environment. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Unpruned Model Parameters** & **AP** & **Puned Model Parameters** & **AP** & **Retrain/Model** \\ \hline DetectNetV2 & 11,200,458 & 0.93515 & 9,561,530 & 0.96316 & 0.85367 \\ FasterRCNN & 12,751,352 & 0.9528 & 10,434,616 & 0.9506 & 0.81831 \\ YOLOv4 & 34,829,183 & 0.9009 & 3,659,191 & 0.9091 & 0.10506 \\ EfficientDet & 3,876,308 & 0.426 & 2,130,676 & 0.426 & 0.54966 \\ DINO & - & 0.83 & - & - & - \\ D-DET & - & 0.71433 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The final evaluation results of the trained model. Figure 18: Virtual fire data generated from multiple real-world environments. In terms of optimizing the site, it is necessary to be very careful not to cause scientific practice, as it has learned the fire data that has occurred virtually at the site. To verify this, we used fire particles to generate virtual data to infer new data not used for learning, and fire images created using fire particles with different shapes were not used when generating virtual data. First, in the case of the former, regardless of the model, confidence was detected as 0.95 or higher without any problems, as shown in Figure 20, and there was no false detection. In addition, in the latter case, we inferred virtual fires and other types of fires to generate learning data, and the results are shown in Figure 21.  
Figure 19: The NVIDIA DGX A100, which was used as a training environment. Figure 20: Inferring new fire footage from fires used to generate data for training. Even though the shape of the flame changed significantly, it exhibited a high detection rate. Similarly, there were a few misdetection results. However, this study learned data for field-optimized models, and even test sets for verification are simply composed of virtual fire data and actual fire data; therefore, it can be interpreted that the model performance indicators of all models are high. In contrast to the high-performance evaluation score, some models did not infer general fire images or were false positives. Figures 22 and 23 show the differences in inference performance between the models. We observed that the YOLOv4 model spotted the initial fire with the fewest false detections, and we learned to utilize resnet50, resnet101, and cspdarknet19 instead of the YOLOv4 model's backbone, resnet18. Table 3 lists the performance evaluation variations when the backbones are different. For ResNet, we can see that the size of the pre-trained model varies greatly as the number of layers increases from 18 to 101. However, after pruning before relearning, Resnet101, which had the largest number of layers, showed fewer parameters than the other backbones. The pruning process of the model optimizes the model to suit reasoning in edge devices such as embedded devices by reducing unnecessary parameters while maintaining the model's performance. As before, when we deduced small initial fire or general fire images apart from the mAP evaluated from the dataset, the sensitivity of the fire was detected depending on the backbone. Figure 24 shows the results.