Deep Learning-Based Multistage Fire Detection System and Emerging Direction
Fires constitute a significant risk to public safety and property, making early and accurate detection essential for an effective response and damage mitigation. Traditional fire detection methods have limitations in terms of accuracy and adaptability, particularly in complex environments in which v...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Fire |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-6255/7/12/451 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846104679563395072 |
|---|---|
| author | Tofayet Sultan Mohammad Sayem Chowdhury Mejdl Safran M. F. Mridha Nilanjan Dey |
| author_facet | Tofayet Sultan Mohammad Sayem Chowdhury Mejdl Safran M. F. Mridha Nilanjan Dey |
| author_sort | Tofayet Sultan |
| collection | DOAJ |
| description | Fires constitute a significant risk to public safety and property, making early and accurate detection essential for an effective response and damage mitigation. Traditional fire detection methods have limitations in terms of accuracy and adaptability, particularly in complex environments in which various fire stages (such as smoke and active flames) need to be distinguished. This study addresses the critical need for a comprehensive fire detection system capable of multistage classification, differentiating between non-fire, smoke, apartment fires, and forest fires. We propose a deep learning-based model using a customized DenseNet201 architecture that integrates various preprocessing steps and explainable AI techniques, such as Grad-CAM++ and SmoothGrad, to enhance transparency and interpretability. Our model was trained and tested on a diverse, multisource dataset, achieving an accuracy of 97%, along with high precision and recall. The comparative results demonstrate the superiority of the proposed model over other baseline models for handling multistage fire detection. This research provides a significant advancement toward more reliable, interpretable, and effective fire detection systems capable of adapting to different environments and fire types, opening new possibilities for environmentally friendly fire type detection, ultimately enhancing public safety and enabling faster, targeted emergency responses. |
| format | Article |
| id | doaj-art-c791ed34abdb4e3da23788f1ebe4b0ce |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-c791ed34abdb4e3da23788f1ebe4b0ce2024-12-27T14:25:44ZengMDPI AGFire2571-62552024-11-0171245110.3390/fire7120451Deep Learning-Based Multistage Fire Detection System and Emerging DirectionTofayet Sultan0Mohammad Sayem Chowdhury1Mejdl Safran2M. F. Mridha3Nilanjan Dey4Department of Computer Science & Engineering, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science & Engineering, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi ArabiaDepartment of Computer Science & Engineering, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science and Engineering, Techno International New Town, Kolkata 700156, IndiaFires constitute a significant risk to public safety and property, making early and accurate detection essential for an effective response and damage mitigation. Traditional fire detection methods have limitations in terms of accuracy and adaptability, particularly in complex environments in which various fire stages (such as smoke and active flames) need to be distinguished. This study addresses the critical need for a comprehensive fire detection system capable of multistage classification, differentiating between non-fire, smoke, apartment fires, and forest fires. We propose a deep learning-based model using a customized DenseNet201 architecture that integrates various preprocessing steps and explainable AI techniques, such as Grad-CAM++ and SmoothGrad, to enhance transparency and interpretability. Our model was trained and tested on a diverse, multisource dataset, achieving an accuracy of 97%, along with high precision and recall. The comparative results demonstrate the superiority of the proposed model over other baseline models for handling multistage fire detection. This research provides a significant advancement toward more reliable, interpretable, and effective fire detection systems capable of adapting to different environments and fire types, opening new possibilities for environmentally friendly fire type detection, ultimately enhancing public safety and enabling faster, targeted emergency responses.https://www.mdpi.com/2571-6255/7/12/451fire detectiondeep learningcomputer visionconvolutional neural networkexplainable AI |
| spellingShingle | Tofayet Sultan Mohammad Sayem Chowdhury Mejdl Safran M. F. Mridha Nilanjan Dey Deep Learning-Based Multistage Fire Detection System and Emerging Direction Fire fire detection deep learning computer vision convolutional neural network explainable AI |
| title | Deep Learning-Based Multistage Fire Detection System and Emerging Direction |
| title_full | Deep Learning-Based Multistage Fire Detection System and Emerging Direction |
| title_fullStr | Deep Learning-Based Multistage Fire Detection System and Emerging Direction |
| title_full_unstemmed | Deep Learning-Based Multistage Fire Detection System and Emerging Direction |
| title_short | Deep Learning-Based Multistage Fire Detection System and Emerging Direction |
| title_sort | deep learning based multistage fire detection system and emerging direction |
| topic | fire detection deep learning computer vision convolutional neural network explainable AI |
| url | https://www.mdpi.com/2571-6255/7/12/451 |
| work_keys_str_mv | AT tofayetsultan deeplearningbasedmultistagefiredetectionsystemandemergingdirection AT mohammadsayemchowdhury deeplearningbasedmultistagefiredetectionsystemandemergingdirection AT mejdlsafran deeplearningbasedmultistagefiredetectionsystemandemergingdirection AT mfmridha deeplearningbasedmultistagefiredetectionsystemandemergingdirection AT nilanjandey deeplearningbasedmultistagefiredetectionsystemandemergingdirection |