Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

Nguyen, Manh Dung; Vu, Hoai Nam; Pham, Duc Cuong; Choi, Bok‐Gil; Ro, Soonghwan

doi:10.1109/access.2021.3122346

Cited by 28 publications

(7 citation statements)

References 24 publications

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“…RYU et al [8] used Harris corner detector and HSV channel to pre-process the flame, and then captured features from Inceptionv3 model to improve the accuracy, but the preprocessing took a long time. Nguyen et al [9] developed a method which combines the CNN and Bi-LSTM to extract spatial domain and temporal domain features of flame simultaneously. However, the large number of fully connected layers in the network made the computation heavy, and made it difficult to deploy.…”

Section: Related Workmentioning

confidence: 99%

A Fire and Smoke Detection Model Based on YOLOv8 Improvement

Gao

2024

IJACSA

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The warning of fire and smoke provides security for people's lives and properties. The utilization of deep learning for fire and smoke warning has been an active area of research, especially the use of target detection algorithms has achieved significant results. For improving the fire and smoke detection performance of model in different scenarios, a high-precision and lightweight improvement based on the model of You Only Look Once (YOLO), is developed. It utilizes partial convolutions to reduce the complexity of model, and add an attention block to acquire the cross-space learning capability. In addition, the neck network is redesigned to realize bidirectional feature fusion. Experiments show that it has significantly improved the results for all metrics in the public Fire-Smoke dataset, and the size of the model has also been widely reduced. Comparisons with other popular target detection models under the same conditions indicate that the improved model has the best performance as well. In order to have a more visual comparison with the detectability of the original model, the heatmap experiments are also established, which also demonstrate that it is characterized by less leakage rate and more focused attention.

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Section: Related Workmentioning

confidence: 99%

A Fire and Smoke Detection Model Based on YOLOv8 Improvement

Gao

2024

IJACSA

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“…Traditional fire alarm systems, such as photoionization smoke detectors, infrared thermal imagers, flame gas sensors, and smoke gas sensors, have drawbacks such as delayed response time and restricted sensor density. Especially in open spaces, airflow and other conditions may impede accurate detection [3].…”

Section: Introductionmentioning

confidence: 99%

FCM-DETR: An Efficient End-to-End Fire Smoke and Human Detection based on Deformable DETR

Liang,

Zeng

2024

Preprint

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Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

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“…Cao Y. [5] and D. Nguyen M. [6] explore the application of Recurrent Neural Networks (RNNs) in fire detection tasks, utilizing their ability to extract relationships between features of the same object across different frames, thereby offering long-term memory of video information. Long Short-Term Memory networks (LSTMs), a variant of RNNs, address the issue of vanishing gradients present in traditional RNN models.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

Liu,

Zhang,

Zhong

et al. 2024

OJAppS

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The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network's bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.

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Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

Cited by 28 publications

References 24 publications

A Fire and Smoke Detection Model Based on YOLOv8 Improvement

A Fire and Smoke Detection Model Based on YOLOv8 Improvement

FCM-DETR: An Efficient End-to-End Fire Smoke and Human Detection based on Deformable DETR

YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

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