Smart City Gnosys
Smart city article details
| Title | Early Smoke And Flame Detection Based On Transformer |
|---|---|
| ID_Doc | 21573 |
| Authors | Wang X.; Li M.; Gao M.; Liu Q.; Li Z.; Kou L. |
| Year | 2023 |
| Published | Journal of Safety Science and Resilience, 4, 3 |
| DOI | http://dx.doi.org/10.1016/j.jnlssr.2023.06.002 |
| Abstract | Fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart cities. Early fire detection is imperative to mitigate potential hazards and minimize associated losses. However, existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection accuracy. To address this issue, in this study a decoder-free fully transformer-based (DFFT) detector is used to achieve early smoke and flame detection, improving the detection performance for fires of different sizes. This method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map prediction. First, data augmentation is performed to enhance the generalizability of the model. Second, the detection-oriented transformer (DOT) backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales, which are then fed into an encoder-only single-layer dense prediction module. Finally, the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder (SAE). The prediction module then aligns the classification and regression features using a task-aligned encoder (TAE) to ensure the semantic interaction of the classification and regression predictions. Experimental results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes, particularly early small fires. The DFFT achieved mean average precision (mAP) values of 87.40% and 81.12% for the two datasets, outperforming other baseline models. It exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features. © 2023 The Authors |
| Author Keywords | Early fire; Fire detection; Public safety; Smoke and flame detection; Vision transformer |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
26592  | 0.891 | He Y.; Sahma A.; He X.; Wu R.; Zhang R. | Firenet: A Lightweight And Efficient Multi-Scenario Fire Object Detector | Remote Sensing, 16, 21 (2024) |
| 26883 | 0.882 | Karthi M.; Priscilla R.; Subhashini G.; Infantia C.N.; Abijith G.R.; Vinisha J. | Forest Fire Detection: A Comparative Analysis Of Deep Learning Algorithms | Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023 (2023) |
| 26572 | 0.881 | Ud D.S.; Muhammad S.; Han F.; Shoaib H.; Ather Iqbal H.M. | Fire Classification Using Attention-Based Deep-Cnn In Digital Images | Proceedings - 2023 10th International Conference on Electrical and Electronics Engineering, ICEEE 2023 (2023) |
| 25423 | 0.877 | Saponara S.; Elhanashi A.; Gagliardi A. | Exploiting R-Cnn For Video Smoke/Fire Sensing In Antifire Surveillance Indoor And Outdoor Systems For Smart Cities | Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020) |
| 61276 | 0.877 | Nadeem M.; Dilshad N.; Alghamdi N.S.; Dang L.M.; Song H.-K.; Nam J.; Moon H. | Visual Intelligence In Smart Cities: A Lightweight Deep Learning Model For Fire Detection In An Iot Environment | Smart Cities, 6, 5 (2023) |
| 26575 | 0.874 | Avazov, K; Mukhiddinov, M; Makhmudov, F; Cho, YI | Fire Detection Method In Smart City Environments Using A Deep-Learning-Based Approach | ELECTRONICS, 11, 1 (2022) |
| 3980 | 0.869 | Tamilselvi M.; Ramkumar G.; Thandaiah Prabu R.; Anitha G.; Mohanavel V. | A Real-Time Fire Recognition Technique Using A Improved Convolutional Neural Network Method | Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023 (2023) |
| 59290 | 0.867 | Hussain T.; Dai H.; Gueaieb W.; Sicklinger M.; De Masi G. | Uav-Based Multi-Scale Features Fusion Attention For Fire Detection In Smart City Ecosystems | ISC2 2022 - 8th IEEE International Smart Cities Conference (2022) |
| 5859 | 0.863 | Norkobil Saydirasulovich S.; Abdusalomov A.; Jamil M.K.; Nasimov R.; Kozhamzharova D.; Cho Y.-I. | A Yolov6-Based Improved Fire Detection Approach For Smart City Environments | Sensors, 23, 6 (2023) |
| 21567 | 0.863 | Kim H.-C.; Lam H.-K.; Lee S.-H.; Ok S.-Y. | Early Fire Detection System By Using Automatic Synthetic Dataset Generation Model Based On Digital Twins | Applied Sciences (Switzerland), 14, 5 (2024) |