Evaluation of synthetic data impact on fire segmentation models performance
Abstract Timely fire detection in industrial environments is crucial to safeguarding people and property. Deep neural networks have shown effectiveness in fire detection over traditional methods. However, they require high-quality datasets, which are costly and time-intensive to gather. To overcome...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01571-5 |
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| author | Matej Arlovic Franko Hrzic Mitesh Patel Tomasz Bednarz Josip Balen |
| author_facet | Matej Arlovic Franko Hrzic Mitesh Patel Tomasz Bednarz Josip Balen |
| author_sort | Matej Arlovic |
| collection | DOAJ |
| description | Abstract Timely fire detection in industrial environments is crucial to safeguarding people and property. Deep neural networks have shown effectiveness in fire detection over traditional methods. However, they require high-quality datasets, which are costly and time-intensive to gather. To overcome this issue, we created the SYN-FIRE dataset, consisting of 2000 labeled images of simulated indoor industrial fires using NVIDIA Omniverse. By using U-Net++ as the baseline, this study explores the impact of the new SYN-FIRE dataset on models’ performance when combined with four publicly available datasets. Two ablation studies were conducted: one replacing portions of real data from publicly available datasets with synthetic data and the other adding various amounts of synthetic data. With over 200 models trained across three resolutions, the results indicate that incorporating additional synthetic data improved DiceScore by $$2.06\%$$ to $$16.09\%$$ (FireBot and BowFire datasets, respectively) while substituting real data with synthetic data generally enhanced performance but with exceptions. Furthermore, tests on challenging real-life fire images confirmed that synthetic data boosts model generalization, supported by GRAD-CAM saliency maps. Finally, we provide key takeaways that point out the main findings of our research. The SYN-FIRE dataset is publicly available to encourage further research in fire detection and prevention. |
| format | Article |
| id | doaj-art-7763aeb2f83547adb2bf3870141e6ad3 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7763aeb2f83547adb2bf3870141e6ad32025-08-20T03:10:13ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-01571-5Evaluation of synthetic data impact on fire segmentation models performanceMatej Arlovic0Franko Hrzic1Mitesh Patel2Tomasz Bednarz3Josip Balen4University of J.J. Strossmayer Osijek, Faculty of Electrical Engineering, Computer Science and Information TechnologyDepartment of Orthopaedic Surgery and Sports Medicine, Boston Children’s Hospital, Harvard Medical SchoolNVIDIA CorporationNVIDIA CorporationUniversity of J.J. Strossmayer Osijek, Faculty of Electrical Engineering, Computer Science and Information TechnologyAbstract Timely fire detection in industrial environments is crucial to safeguarding people and property. Deep neural networks have shown effectiveness in fire detection over traditional methods. However, they require high-quality datasets, which are costly and time-intensive to gather. To overcome this issue, we created the SYN-FIRE dataset, consisting of 2000 labeled images of simulated indoor industrial fires using NVIDIA Omniverse. By using U-Net++ as the baseline, this study explores the impact of the new SYN-FIRE dataset on models’ performance when combined with four publicly available datasets. Two ablation studies were conducted: one replacing portions of real data from publicly available datasets with synthetic data and the other adding various amounts of synthetic data. With over 200 models trained across three resolutions, the results indicate that incorporating additional synthetic data improved DiceScore by $$2.06\%$$ to $$16.09\%$$ (FireBot and BowFire datasets, respectively) while substituting real data with synthetic data generally enhanced performance but with exceptions. Furthermore, tests on challenging real-life fire images confirmed that synthetic data boosts model generalization, supported by GRAD-CAM saliency maps. Finally, we provide key takeaways that point out the main findings of our research. The SYN-FIRE dataset is publicly available to encourage further research in fire detection and prevention.https://doi.org/10.1038/s41598-025-01571-5Deep learningFire datasetFire detectionIndustrial fireSYN-FIRESynthetic data |
| spellingShingle | Matej Arlovic Franko Hrzic Mitesh Patel Tomasz Bednarz Josip Balen Evaluation of synthetic data impact on fire segmentation models performance Scientific Reports Deep learning Fire dataset Fire detection Industrial fire SYN-FIRE Synthetic data |
| title | Evaluation of synthetic data impact on fire segmentation models performance |
| title_full | Evaluation of synthetic data impact on fire segmentation models performance |
| title_fullStr | Evaluation of synthetic data impact on fire segmentation models performance |
| title_full_unstemmed | Evaluation of synthetic data impact on fire segmentation models performance |
| title_short | Evaluation of synthetic data impact on fire segmentation models performance |
| title_sort | evaluation of synthetic data impact on fire segmentation models performance |
| topic | Deep learning Fire dataset Fire detection Industrial fire SYN-FIRE Synthetic data |
| url | https://doi.org/10.1038/s41598-025-01571-5 |
| work_keys_str_mv | AT matejarlovic evaluationofsyntheticdataimpactonfiresegmentationmodelsperformance AT frankohrzic evaluationofsyntheticdataimpactonfiresegmentationmodelsperformance AT miteshpatel evaluationofsyntheticdataimpactonfiresegmentationmodelsperformance AT tomaszbednarz evaluationofsyntheticdataimpactonfiresegmentationmodelsperformance AT josipbalen evaluationofsyntheticdataimpactonfiresegmentationmodelsperformance |