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All Journal International Journal of Advances in Applied Sciences JOIV : International Journal on Informatics Visualization
Rahman, Shakila
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Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning Rahman, Shakila; Jamee, Syed muhammad Hasnat; Rafi, Jakaria Khan; Juthi, Jafrin Sultana; Sajib, Abdul Aziz; Uddin, Jia
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp987-999

Abstract

Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperforming some baseline models- YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2 in the precision, recall, and mean average precision (mAP) metrics. The proposed method achieves 68.3% precision, 54.6% recall, 60.7% F1 score, and 57.3% mAP. Integrating YOLOv8 in fire and smoke detection systems can significantly improve response times, enhance the ability to mitigate fire outbreaks, and potentially save lives and property. This research advances fire detection systems and establishes a precedent for applying deep learning techniques to critical safety applications, pushing the boundaries of innovation in public safety.
Comparative Study of two Region-based Detection Models, Faster R-CNN and R-FCN in detecting Smoke Region from Several Environmental Conditions Benta Hasan, Sumayea; Rahman, Shakila; Khaliluzzaman, Md; Binti Abdul Aziz, Nor Hidayati; Jakir Hossen, Md
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3764

Abstract

Detecting smoke covering different environmental situations is an explanatory task while mitigating the loss of lives or reducing the fire-based disaster. Various approaches were experimented with to tackle this task. A few of them are convenient, whereas many fail in outcomes. To that end, this research presents a comparative analysis of two region-based detection models based on deep neural networks. Faster R-CNN denotes the Region-based Convolutional Neural Network, and R-FCN, which constitutes the Region-based Fully Convolutional Network, is employed in this research to evaluate detection performance for the smoke detection task. The analysis demonstrates these models with respect to detection accuracy, precision, recall, Intersection over Union, detection speed, and resilience to challenging conditions (e.g., variations in lighting, weather, and complex backgrounds). Research results highlight Faster R-CNN's accuracy, which supports applications in fire-smoke prevention, whereas R-FCN focuses on detection speed, which is relevant to the smoke-monitoring sector. The key issue to consider is the trade-off between computational efficiency and detection accuracy. Performance in extreme environmental conditions can be enhanced through further advances with regard to data variability and typical challenging scenarios. Moreover, Faster R-CNN and R-FCN achieved Precision values of 96.72% and 95.46%, Recall values of 97.66% and 95.73%, and F1-Score values of 97.18% and 95.59%, respectively. This study assumes further assessments to ensure the safety of the living.
Co-Authors Benta Hasan, Sumayea Binti Abdul Aziz, Nor Hidayati Jakir Hossen, Md Jamee, Syed muhammad Hasnat Juthi, Jafrin Sultana Khaliluzzaman, Md Rafi, Jakaria Khan Sajib, Abdul Aziz Uddin, Jia