Enhancing Fire Detection Technology: A UV-Based System Utilizing Fourier Spectrum Analysis for Reliable and Accurate Fire Detection

Truong, Cong Tuan; Nguyen, Thanh-Hung; Vu, Van Quang; Hoàng, Việt; Nguyen, Duc-Toan

doi:10.3390/app13137845

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…The algorithms integrated into OFDs play a crucial role in enhancing their flame detection capabilities. During the past decades, various algorithms for OFDs have been proposed, including those based on infrared [ 4 , 6 , 37 ], ultraviolet [ 7 , 38 , 39 ], and infrared/ultraviolet [ 5 , 8 , 40 ] technologies. Liu et al proposed a detector of flame/smoke video image detection system consisting of one infrared camera and others [ 6 ].…”

Section: Related Work and Background Techniquesmentioning

confidence: 99%

“…Liu et al proposed a detector of flame/smoke video image detection system consisting of one infrared camera and others [ 6 ]. Truong et al proposed a low-cost and reliable smart fire alarm system that utilizes ultraviolet detection technology [ 7 ]. Genovese et al proposed an image processing system for the detection of wildfire smoke based on computational intelligence techniques by infrared/ ultraviolet cameras [ 8 ].…”

Section: Related Work and Background Techniquesmentioning

confidence: 99%

“…OFDs are capable of successfully fusing particular feature fusion algorithms, such as scale invariant feature transform (SIFT) [ 1 ], flame detection algorithm based on multi-feature fusion (FDAMF) [ 2 ], and fire color feature extraction (FCFE) [ 3 ], with optical sensor hardware, such as [ 4 , 5 ], and so on. These OFDs, i.e., infrared [ 6 ], ultraviolet [ 7 ], and infrared/ultraviolet [ 8 ], are frequently used in the community of firefighting [ 9 ], medical [ 10 ], and agricultural [ 11 ]. OFDs integrate optical sensors and algorithms for flame detection in diverse applications.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames

Tao,

Wang,

Liu

et al. 2024

PLoS ONE

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Traditional optical flame detectors (OFDs) in flame detection are susceptible to environmental interference, which will inevitably cause detection errors and miscalculations when confronted with a complex environment. The conventional deep learning-based models can mitigate the interference of complex environments by flame image feature extraction, which significantly improves the precision of flame recognition. However, these models focus on identifying the general profile of the static flame, but neglect to effectively locate the source of the dynamic flame. Therefore, this paper proposes a novel dynamic flame detection method named Dynamic Deformable Adaptive Framework (DDAF) for locating the flame root region dynamically. Specifically, to address limitations in flame feature extraction of existing detection models, the Deformable Convolution Network v2 (DCNv2) is introduced for more flexible adaptation to the deformations and scale variations of target objects. The Context Augmentation Module (CAM) is used to convey flame features into Dynamic Head (DH) to feature extraction from different aspects. Subsequently, the Layer-Adaptive Magnitude-based Pruning (LAMP) where the connection with the smallest LAMP score is pruned sequentially is employed to further enhance the speed of model detection. More importantly, both the coarse- and fine-grained location techniques are designed in the Inductive Modeling (IM) to accurately delineate the flame root region for effective fire control. Additionally, the Temporal Consistency-based Detection (TCD) contributes to improving the robustness of model detection by leveraging the temporal information presented in consecutive frames of a video sequence. Compared with the classical deep learning method, the experimental results on the custom flame dataset demonstrate that the AP0.5 value is improved by 4.4%, while parameters and FLOPs are reduced by 25.3% and 25.9%, respectively. The framework of this research extends applicability to a variety of flame detection scenarios, including industrial safety and combustion process control.

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Section: Related Work and Background Techniquesmentioning

confidence: 99%

Section: Related Work and Background Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames

Tao,

Wang,

Liu

et al. 2024

PLoS ONE

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“…If the bounding boxes around the classes of interest were too loosely defined, this could force the models to generalize on a false assumption. Conversely, very stringent bounding boxes could result in missing a section of the relevant class, again leading to the risk of false generalization during training [ 3 ]. To surmount these two risks, we used data annotations based on closed proximity.…”

Section: Proposed Workmentioning

confidence: 99%

“…These sensors can detect a fire when it evolves, and fumes and smoke lead to flames, but it is risky to let a fire expand to such an extent that it can lead to serious damage. A 10 min delay in putting out a fire in the engine room may cost USD 200,000, while a 20 min delay may cost up to USD 2,000,000 [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Avazov

Jamil

Muminov

et al. 2023

Sensors

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Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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A MATTER-Enabled IoT Framework for Enhanced Fire Detection and Real-Time Decision-Making

Bhardwaj,

Joshi

2024

SN COMPUT. SCI.

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Enhancing Fire Detection Technology: A UV-Based System Utilizing Fourier Spectrum Analysis for Reliable and Accurate Fire Detection

Cited by 8 publications

References 29 publications

A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames

A deep learning-based dynamic deformable adaptive framework for locating the root region of the dynamic flames

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

A MATTER-Enabled IoT Framework for Enhanced Fire Detection and Real-Time Decision-Making

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