## 3 Results and Discussion
### Evaluation of Flame Segmentation Performance  
The flame segmentation performance of the models used in this study was evaluated using six fire experiment videos provided by the NIST before predicting the HRR from the fire videos. Flame characteristics affect the segmentation performance; therefore, three fire periods in each fire video were defined: growth period (0-75% of peak HRR), fully developed period (\(\geq\)75% of peak HRR), and decay period (post-peak HRR, \(<\)75% of peak HRR). The segmentation performance of each model was quantitatively assessed during these fire periods using the IoU metric. As representative segmentation results of the box fire experiments, Figure 7a shows the flame segmentation results from the one-box fire video, and Figure 7b,c show those for the YOLO-YCbCr and YOLO-seg models, respectively. The IoU values for the YOLO-YCbCr model during the growth, fully developed, and decay periods were 32.6%, 54.4%, and 39.7%, respectively, whereas those for the YOLO-seg model were 28.8%, 58.8%, and 35.6%, respectively. These results confirm that the YOLO-YCbCr model outperformed the YOLO-seg model during the growth and decay phases. This greater performance can be attributed to the YOLO-YCbCr model's ability to differentiate flames more effectively by utilizing pixel-based color information in fire images, including small and complex flames. In contrast, during the fully developed period, the YOLO-seg model had better segmentation performance than the YOLO-YCbCr model, due to its strong segmentation capability for larger flames with clearer boundaries. In the four-boxes fire video, the IoU values for the YOLO-YCbCr model during the growth, fully developed, and decay periods were 35.2%, 66.1%, and 43.0%, respectively, whereas those for the YOLO-seg model were 30.2%, 69.4%, and 33.6%, respectively. Figure 6: Flame segmentation results: (**a**) forest fire, (**b**) building fire, (**c**) outdoor fire, and (**d**) indoor fire. Figure 8 shows the flame segmentation results from the one-wood-crib fire video, selected as a representative segmentation result for the wood-crib fire experiments. The IoU values for the YOLO-YCbCr model across the growth, fully developed, and decay periods were 59.6%, 56.7%, and 42.4%, respectively, whereas the YOLO-seg model achieved 48.9%, 66.1%, and 33.3%, respectively. These results follow a similar trend to the box fire segmentation results, with the YOLO-YCbCr model performing better in the growth and decay periods and the YOLO-seg model showing relatively higher accuracy in the fully developed period. In the four-wood-cribs fire video, the IoU values for the YOLO-YCbCr model during the growth, fully developed, and decay periods were 57.1%, 71.8%, and 48.7%, respectively, whereas the YOLO-seg model achieved 54.6%, 73.7%, and 47.3%, respectively. As a representative segmentation result of the corn oil pool fire experiments, the segmentation results for the corn oil pool fire in the Calphalon(r) pan, illustrated in Figure 9, indicate that the YOLO-YCbCr model achieved mean IoU values of 67.5%, 68.5%, and 72.8% during the growth, fully developed, and decay periods, respectively. In comparison, the YOLO-seg model achieved values of 48.6%, 68.1%, and 59.4% for the corresponding  
Figure 8: One-wood-crib fire experiment flame segmentation results: (**a**) original images, (**b**) YOLO-YCbCr model, and (**c**) YOLO-seg model. Figure 7: One-box fire experiment flame segmentation results: (**a**) original images, (**b**) YOLO-YCbCr model, and (**c**) YOLO-seg model. periods. The relatively more consistent flame color in the corn oil pool fire enhanced the flame segmentation performance throughout the entire fire period compared to that for the solid-fuel fires. In the corn oil pool fire in the aluminum pan, the IoU values for the YOLO-YCbCr model during the growth, fully developed, and decay periods were 58.0%, 56.8%, and 59.7%, respectively, whereas those for the YOLO-seg model were 53.4%, 68.2%, and 61.6%, respectively.