article · Neural Computing and Applications
This research addresses the challenges of traditional fire detection methods in smart cities, which often suffer from limited accuracy and speed, leading to devastating consequences. It proposes an improved fire detection approach called the smart fire detection system (SFDS), which is based on the YOLOv8 algorithm. The SFDS leverages deep learning to identify fire-specific features in real time, aiming to enhance detection accuracy, minimise false alarms, and be cost-effective. The system's framework comprises Application, Fog, Cloud, and IoT layers, enabling real-time data processing for quicker responses. The SFDS demonstrated state-of-the-art performance, achieving a high precision rate of 97.1% for all classes.
Effective fire detection is crucial for protecting lives and property in urban environments. This research offers a more accurate and faster system, potentially reducing the impact of fires and improving overall safety in smart cities. Its real-time capabilities could lead to quicker emergency responses.
This research presents an early-stage, applied technology with clear potential applications in smart city infrastructure. It could be used for fire safety management in public areas, forest fire monitoring, and intelligent security systems. The system's ability to detect other hazards like gas leaks or flooding suggests broader utility for urban safety organisations and technology providers developing smart city solutions.
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Abstract Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.
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DOI: 10.1007/s00521-023-08809-1
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