## 3 Results and Discussion
### Performance Comparison with Sequence Modeling-Based HRR Prediction Model  
A quantitative performance comparison between our proposed HRR prediction model and a prior sequence modeling-based model was conducted. The selected prior model proposed by [PERSON] et al. [29] utilizes a VGG16-based architecture trained on paired flame  
Figure 14: HRR prediction results of the proposed models: (**a**) without the AFH and (**b**) with the AFH.  
images and HRR data to predict real-time HRR through temporal sequence modeling. Two experimental videos for trash-can fire and wood-pallet fire experiments, originally provided by the NIST and also utilized in the prior study, were selected for performance comparison. Figure 15a shows the HRR prediction results of the trash-can fire experiments. The MAE values for the YOLO-YCbCr and YOLO-seg models were 25.0 kW and 20.7 kW, respectively, which are greater than that of the prior study, which had an MAE of 8.5 kW. This was attributed to fluctuations in the HRR values caused by frame-wise flame-segmentation-based HRR prediction and relatively poor segmentation performance during the peak HRR period. The proposed models exhibited good tracking of the experimental HRR variations during the growth and decay periods, but not for the fully developed period. Furthermore, the \(R^{2}\) values were 0.73 and 0.83 for the YOLO-YCbCr and YOLO-seg models, respectively, which are lower than that of the prior study, which had an \(R^{2}\) of 0.97. Figure 15b shows the HRR prediction results of the wood-pallet fire experiments. The MAE values for the YOLO-YCbCr and YOLO-seg models were 315.6 kW and 328.0 kW, respectively, which are greater than that of the prior study, which had an MAE of 225 kW due to the high fluctuations in the HRR values. However, both proposed models exhibited good tracking of the experimental HRR variations, and the \(R^{2}\) values were 0.80 and 0.78 for the YOLO-YCbCr and YOLO-seg models, respectively. The \(R^{2}\) values of the proposed models were greater than that of the prior study, which reported an \(R^{2}\) of 0.68 due to the underestimated HRR prediction of the prior model during the fully developed period. This was mainly because of fewer images from HRR paired datasets for large-HRR fires during training [29].  
In summary, the proposed model can predict HRR values by extracting flame features. The model also benefits from simpler data requirements and lower training costs, as it is  
Figure 15: HRR prediction results: (**a**) trash-can fire experiment and (**b**) wood-pallet fire experiment [29].  
trained solely on flame images rather than large-scale HRR-labeled time-series data. Compared to the sequence-based model, which underperforms in out-of-distribution scenarios due to high data dependency, the proposed approach demonstrates more consistent HRR prediction even in complex or large-scale fire events. However, since the model does not account for temporal dynamics, the predicted HRR tends to exhibit significant fluctuations, and the real-time prediction performance is relatively lower than that of the sequence modeling-based HRR prediction model. Although applying the AFH calculation helps to reduce this fluctuation, further enhancements are needed to address this issue. Thus, future studies will incorporate temporal modeling techniques and enhance the AFH's applicability to better predict transient fire HRR behavior.