Computer Vision for Fire Detection on UAVs—From Software to Hardware

Moumgiakmas, Seraphim S.; Samatas, Gerasimos G.; Papakostas, George A.

doi:10.3390/fi13080200

Cited by 27 publications

(11 citation statements)

References 61 publications

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“…In [16], vision-based indoor fre and smoke detection systems were created using tiny training datasets and photos with diferent pixel densities. Unmanned aerial vehicle (UAV) of computer vision-based fre detection was investigated in [17], with a focus on early identifcation and prevention of fre threats. CNNs and smoke motion properties-based algorithms for recognizing fames were proposed in [18].…”

Section: Integration With Iot and Smartmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

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Section: Integration With Iot and Smartmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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“…The collected data are stored and processed in a server. Additionally, UAV imaging can recognize fire risks and prevent fires in a timely manner [73].…”

Section: Disastersmentioning

confidence: 99%

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

Fourati¹,

Alsamhi²,

Alouini³

2022

Preprint

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Connectivity in rural areas is one of the main challenges of communication networks. To overcome this challenge, a variety of solutions for different situations are required. Optimizing the current networking paradigms is therefore mandatory. The high costs of infrastructure and the low revenue of cell sites in rural areas compared with urban areas are especially unattractive for telecommunication operators. Therefore, space, air, and ground networks should all be optimized for achieving connectivity in rural areas. We highlight the latest works on rural connectivity, discuss the solutions for terrestrial networks, and study the potential benefits of nonterrestrial networks. Furthermore, we present an overview of artificial intelligence (AI) techniques for improving space, air, and ground networks, hence improving connectivity in rural areas. AI enables intelligent communications and can integrate space, air, and ground networks for rural connectivity. We discuss the rural connectivity challenges and highlight the latest projects and research and the empowerment of networks using AI. Finally, we discuss the potential positive impacts of providing connectivity to rural communities.

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“…One of the key components of these systems is the real-time detection algorithm, because the algorithm determines detection accuracy and speed [ 6 ]. The use of computer vision algorithms, has significantly improved the accuracy and efficiency of coal mine fire detection [ 7 ]. This is essential for detecting potential hazards and ensuring the timely response to mitigate any potential damage.…”

Section: Introductionmentioning

confidence: 99%

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

Kong,

Li,

Duan

2024

PLoS ONE

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Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model’s detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model’s generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

Cited by 27 publications

References 61 publications

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

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