## 3 Results and Discussion
### Evaluation of Fire Heat Release Rate Prediction Performance  
A real-time HRR prediction was conducted using the six fire experiment videos provided by the NIST. The predicted HRR values were compared with experimental values to evaluate the performance of the HRR prediction models. Figure 10 shows the HRR prediction results of the box fire experiments. In the one-box fire experiment shown in Figure 10a, the MAE values for the YOLO-YCbCr and YOLO-seg models were 37.99 kW and 47.27 kW, respectively. Both models exhibited good tracking of the experimental HRR variations over time. Furthermore, the \(R^{2}\) values were 0.83 and 0.84 for the YOLO-YCbCr and YOLO-seg models, respectively, indicating good agreement with the experimental data. However, the real-time HRR values predicted by both models fluctuated considerably compared to the experimental results. This is because the fire experiment video provided discontinuous and limited frames, which included intermittent flames, thereby causing large fluctuations in the HRR values. Figure 10 presents the results of the four-boxes fire experiment. The MAE values for the YOLO-YCbCr and YOLO-seg models were 80.33 kW and 87.38 kW, respectively. Both models underpredicted the HRR values near the peak HRR, which could be attributed to unburned parts outside the combustibles obscuring the flames and leading to an underestimated flame size. While the YOLO-seg model  
Figure 9: Coil oil pool fire in Calphalón® pan experiment flame segmentation results: (**a**) original images, (**b**) YOLO-YCbCr model, and (**c**) YOLO-seg model. achieved an \(R^{2}\) value of 0.89, indicating high predictive accuracy, the YOLO-YCbCr model exhibited a lower \(R^{2}\) value of 0.74. This performance degradation was attributed to the occurrence of various colored flames, which led to reduced segmentation performance of the color-based YCbCr rules and, consequently, resulted in more variations in flame height estimation. Figure 11 presents the HRR prediction results of the wood-fire experiments. In the one-wood-crib fire experiment shown in Figure 11a, the MAE values for the YOLO-YCbCr and YOLO-seg models were 65.92 kW and 60.13 kW, respectively. The HRR values predicted by both models were underestimated during the decay phase compared to the experimental HRR values. This discrepancy was attributed to the attenuation of the visible flame during this phase, caused by combustion occurring near or within the fuel surface. Accurate flame region prediction was more challenging for the models under these conditions. The \(R^{2}\) values for the YOLO-YCbCr and YOLO-seg models were 0.77 and 0.87, respectively. Figure 11b shows the HRR prediction results for the four-wood-cribs fire experiment. The MAE values for the YOLO-YCbCr and YOLO-seg models were 147.98 kW and 132.25 kW, respectively. The MAE values remained high due to the proposed frame-wise HRR prediction approach, with a limited number of frames provided in the fire videos, which led to the capture of intermittent flames and, consequently, resulted in fluctuating HRR predictions. Both models accurately predicted the HRR values in real time, with the YOLO-YCbCr and YOLO-seg models achieving \(R^{2}\) values of 0.86 and 0.90, respectively. Figure 10: HRR prediction results for the box fire experiments: (**a**) one box and (**b**) four boxes. Figure 12 presents the HRR prediction results from the corn oil pool fire experiments. As shown in Figure 12a, the MAE values for the YOLO-YCbCr and YOLO-seg models for the fire experiment involving corn oil in an aluminum pan were 4.10 kW and 5.14 kW, respectively; the \(R^{2}\) values were 0.82 and 0.70, respectively. Although both models effectively captured the overall trend of the experimental HRR curves, the HRR values were underpredicted. This underestimation could be attributed to the partial obstruction of the flame by the pan's sidewalls, which hindered accurate flame height prediction and, consequently, resulted in a lower HRR prediction.