Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment
The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire de...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/6/5/103 |
| _version_ | 1797572286007476224 |
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| author | Muhammad Nadeem Naqqash Dilshad Norah Saleh Alghamdi L. Minh Dang Hyoung-Kyu Song Junyoung Nam Hyeonjoon Moon |
| author_facet | Muhammad Nadeem Naqqash Dilshad Norah Saleh Alghamdi L. Minh Dang Hyoung-Kyu Song Junyoung Nam Hyeonjoon Moon |
| author_sort | Muhammad Nadeem |
| collection | DOAJ |
| description | The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection. |
| first_indexed | 2024-03-10T20:54:05Z |
| format | Article |
| id | doaj.art-cfdeddd6a40b43aaa1011d3beb59f1f9 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-10T20:54:05Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-cfdeddd6a40b43aaa1011d3beb59f1f92023-11-19T18:06:48ZengMDPI AGSmart Cities2624-65112023-08-01652245225910.3390/smartcities6050103Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT EnvironmentMuhammad Nadeem0Naqqash Dilshad1Norah Saleh Alghamdi2L. Minh Dang3Hyoung-Kyu Song4Junyoung Nam5Hyeonjoon Moon6Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaThe recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.https://www.mdpi.com/2624-6511/6/5/103disaster managementfire monitoringfire classificationdeep learningMobileNetlightweight model |
| spellingShingle | Muhammad Nadeem Naqqash Dilshad Norah Saleh Alghamdi L. Minh Dang Hyoung-Kyu Song Junyoung Nam Hyeonjoon Moon Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment Smart Cities disaster management fire monitoring fire classification deep learning MobileNet lightweight model |
| title | Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment |
| title_full | Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment |
| title_fullStr | Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment |
| title_full_unstemmed | Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment |
| title_short | Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment |
| title_sort | visual intelligence in smart cities a lightweight deep learning model for fire detection in an iot environment |
| topic | disaster management fire monitoring fire classification deep learning MobileNet lightweight model |
| url | https://www.mdpi.com/2624-6511/6/5/103 |
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