RT-DETR for Intelligent Fire Detection: An Applied Framework for Smart Building and IoT Systems
References
- U.S. Fire Administration, “Statistics,” 2025. [Online]. Available: https://www.usfa.fema.gov/statistics/. Accessed: 2025-10-30.
- Curtis, “UK house fire statistics – how many house fires per year?” Blog post on Morgan Clark, 2024. [Online]. Available: https://www.morganclark.co.uk/about-us/blog/uk-house-fire-statistics/. Accessed: 2025-10-30.
- Tony Mariotti, “House fire statistics.” RybyHome. 2023. Accessed: 2025-10-30. https://www.rubyhome.com/blog/house-fire-stats/
- H. Sharma and N. Kanwal, “Intelligent video-based fire detection: A novel dataset and real-time multi-stage classification approach,” Expert Systems with Applications, vol. 271, May 2025, Art. no. 126655. https://doi.org/10.1016/j.eswa.2025.126655
- S. Muksimova, S. Umirzakova, J. Baltayev, and Y.-I. Cho, “Lightweight deep learning model for fire classification in tunnels,” Fire, vol. 8, no. 3, Feb. 2025, Art. no. 85. https://doi.org/10.3390/fire8030085
- F. Hou, X. Rui, Y. Chen, and X. Fan, “Flame and smoke semantic dataset: Indoor fire detection with deep semantic segmentation model,” Electronics, vol. 12, no. 18, Sep. 2023, Art. no. 3778. https://doi.org/10.3390/electronics12183778
- K. Niu, C. Wang, J. Xu, C. Yang, X. Zhou, and X. Yang, “An improved YOLOv5s-Seg detection and segmentation model for the accurate identification of forest fires based on UAV infrared image,” Remote Sensing, vol. 15, no. 19, Sept. 2023, Art. no. 4694. https://doi.org/10.3390/rs15194694
- H. Kwon, S. Choi, W. Woo, and H. Jung, “Evaluating segmentation-based deep learning models for real-time electric vehicle fire detection,” Fire, vol. 8, no. 2, Feb. 2025, Art. no. 66. https://doi.org/10.3390/fire8020066
- N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol. 12346, A. Vedaldi, H. Bischof, T. Brox, and J.M. Frahm, Eds. Springer, Cham, 2020, pp. 213–229. https://doi.org/10.1007/978-3-030-58452-8_13
- Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen, “DETRs beat YOLOs on real-time object detection,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2024, pp. 16965–16974. https://doi.org/10.1109/CVPR52733.2024.01605
- M. Wang, W. Xiao, Y. Wang, H. Jia, Y. Gao, Z. Chen, and F. Zheng, “SCSFish2025: a large dataset from South China sea for coral reef fish identification,” Scientific Reports, vol. 15, Aug. 2025, Art. no. 30091. https://doi.org/10.1038/s41598-025-14785-4
- H. Wang, S. Zhang, C. Zhang, Z. Liu, Q. Huang, X. Ma, and Y. Jiang, “Snake-DETR: a lightweight and efficient model for fine-grained snake detection in complex natural environments,” Scientific Reports, vol. 15, Jan. 2025, Art. no. 1282. https://doi.org/10.1038/s41598-024-84328-w
- X. C. Acaro Chacón, F. Lo Scudo, G. Cappuccino, and C. Dodaro, “RecyBat24: a dataset for detecting lithium-ion batteries in electronic waste disposal,” Scientific Data, vol. 12, May 2025, Art. no. 843. https://doi.org/10.1038/s41597-025-05211-5
- S. K. Nawaz, A. Khader, M. Taher, and K. Mehar, “Real-time object detection using deep learning,” International Journal on Science and Technology, vol. 16, no. 4, Dec. 2025. https://doi.org/10.71097/IJSAT.v16.i4.9814
- S. Zhang, X. Lv, Y. Xing, Q. Wu, D. Xu, C. Zhao, and Y. Zhang, “YOLOIOD: Towards real time incremental object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 15, Mar. 2025, pp. 12744–12752. https://doi.org/10.1609/aaai.v40i15.38271
- M. D. Tehseen, G. Shin, J.-Y. Kim, and Y. Won, “SeeSSD: Computational storage for energy-efficient real-time object detection,” ACM Transactions on Embedded Computing Systems, vol. 25, no. 1, pp. 1–27, Jan. 2026. https://doi.org/10.1145/3774649
- J. Chen, K. Sun, Z. You, and L. Xiang, “EAI-YOLO: faster, and more accurate for real-time dynamic object detection,” Signal, Image and Video Processing, vol. 19, Sep. 2025, Art. no. 1159. https://doi.org/10.1007/s11760-025-04775-4
- S. H. Hozhabr and R. Giorgi, “A survey on real-time object detection on FPGAs,” IEEE Access, vol. 13, pp. 38195–38238, Feb. 2025. https://doi.org/10.1109/ACCESS.2025.3544515
- X. Li, X. Pu, W. Ling, and X. Song, “YOLO-SAM an end-to-end framework for efficient real time object detection and segmentation,” Scientific Reports, vol. 15, Nov. 2025, Art. no. 40854. https://doi.org/10.1038/s41598-025-24576-6
- M. Kamphuis, “Tiny-toxic-detector: A compact transformer-based model for toxic content detection,” arXiv:2409.02114, Aug. 2024. https://doi.org/10.48550/arXiv.2409.02114
- Y. Li, S. Wang, D. Liu, C. Zhou, and Z. Gui, “TransDetector: A transformer-based detector for underwater acoustic differential OFDM communications,” IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 9899–9911, Feb. 2024. https://doi.org/10.1109/TWC.2024.3367179
- B. Peng and T.-K. Kim, “YOLO-HF: Early detection of home fires using YOLO,” IEEE Access, vol. 13, pp. 79451–79466, May 2025. https://doi.org/10.1109/ACCESS.2025.3566907
- M. Glucina, N. Andelic, I. Lorencin, and Z. Car, “Detection and classification of printed circuit boards using YOLO algorithm,” Electronics, vol. 12, no. 3, Jan. 2023, Art. no. 667. https://doi.org/10.3390/electronics12030667
- N. P. Nguyen, P. T. Huynh, K. A. Nguyen, T. T. Pham Tran, T. T. V. Nguyen, and H. T. Nguyen, “Towards reliable early fire and smoke detection using optimized YOLOv11,” in Future Data and Security Engineering (FDSE 2025), T. K. Dang, J. Küng, and T.M. Chung, Eds. Springer Nature Singapore, Nov. 2025. pp. 363–378. https://doi.org/10.1007/978-981-95-4724-1_25
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 1–11. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Language: English
Page range: 138 - 154
Submitted on: Feb 24, 2026
Accepted on: May 26, 2026
Published on: Jun 19, 2026
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
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© 2026 Pham-Thai-Toan Tran, Nhan Phi Nguyen, Kiet Anh Nguyen, Ngoc Huynh Pham, Hai Thanh Nguyen, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.