Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods
Heat release rate (HRR) is a key indicator for characterizing fire behavior, and it is conventionally measured under laboratory conditions. However, this measurement is limited in its widespread application to various fire conditions, due to its high cost, operational complexity, and lack of real-ti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Fire |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-6255/8/7/283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849246522263207936 |
|---|---|
| author | Joohyung Roh Sehong Min Minsuk Kong |
| author_facet | Joohyung Roh Sehong Min Minsuk Kong |
| author_sort | Joohyung Roh |
| collection | DOAJ |
| description | Heat release rate (HRR) is a key indicator for characterizing fire behavior, and it is conventionally measured under laboratory conditions. However, this measurement is limited in its widespread application to various fire conditions, due to its high cost, operational complexity, and lack of real-time predictive capability. Therefore, this study proposes an image-based HRR prediction model that uses deep learning and image processing techniques. The flame region in a fire video was segmented using the YOLO-YCbCr model, which integrates YCbCr color-space-based segmentation with YOLO object detection. For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. The proposed models were applied to various experimental fire videos, and their prediction performances were quantitatively assessed. The results indicated that the proposed models accurately captured the HRR variations over time, and applying the average flame height calculation enhanced the prediction performance by reducing fluctuations in the predicted HRR. These findings demonstrate that the image-based HRR prediction model can be used to estimate real-time HRR values in diverse fire environments. |
| format | Article |
| id | doaj-art-e4c6ce2ed09f45a2b7f117c034adfabe |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-e4c6ce2ed09f45a2b7f117c034adfabe2025-08-20T03:58:27ZengMDPI AGFire2571-62552025-07-018728310.3390/fire8070283Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing MethodsJoohyung Roh0Sehong Min1Minsuk Kong2Department of Equipment and Fire Protection Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Engineering, Major of Equipment and Fire Protection Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Mechanical Engineering, Major of Equipment and Fire Protection Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaHeat release rate (HRR) is a key indicator for characterizing fire behavior, and it is conventionally measured under laboratory conditions. However, this measurement is limited in its widespread application to various fire conditions, due to its high cost, operational complexity, and lack of real-time predictive capability. Therefore, this study proposes an image-based HRR prediction model that uses deep learning and image processing techniques. The flame region in a fire video was segmented using the YOLO-YCbCr model, which integrates YCbCr color-space-based segmentation with YOLO object detection. For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. The proposed models were applied to various experimental fire videos, and their prediction performances were quantitatively assessed. The results indicated that the proposed models accurately captured the HRR variations over time, and applying the average flame height calculation enhanced the prediction performance by reducing fluctuations in the predicted HRR. These findings demonstrate that the image-based HRR prediction model can be used to estimate real-time HRR values in diverse fire environments.https://www.mdpi.com/2571-6255/8/7/283HRR predictionimage processingdeep learningflame segmentationprediction performance |
| spellingShingle | Joohyung Roh Sehong Min Minsuk Kong Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods Fire HRR prediction image processing deep learning flame segmentation prediction performance |
| title | Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods |
| title_full | Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods |
| title_fullStr | Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods |
| title_full_unstemmed | Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods |
| title_short | Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods |
| title_sort | performance evaluation of real time image based heat release rate prediction model using deep learning and image processing methods |
| topic | HRR prediction image processing deep learning flame segmentation prediction performance |
| url | https://www.mdpi.com/2571-6255/8/7/283 |
| work_keys_str_mv | AT joohyungroh performanceevaluationofrealtimeimagebasedheatreleaseratepredictionmodelusingdeeplearningandimageprocessingmethods AT sehongmin performanceevaluationofrealtimeimagebasedheatreleaseratepredictionmodelusingdeeplearningandimageprocessingmethods AT minsukkong performanceevaluationofrealtimeimagebasedheatreleaseratepredictionmodelusingdeeplearningandimageprocessingmethods |