A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires

Choi, Minseok; Kim, Chungeon; Oh, Hyunseok

doi:10.1093/jcde/qwac027

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…An enhanced technique of convolutional neural network (CNN) for smoke and fire detection has been suggested by researchers [14][15][16]. In order to enhance neural networks, researchers have recently used Transformer Backbone networks [17]; common examples of these are ViT (vision transformer) [18], Swin [19], and PVT (pyramid vision transformer) [20].…”

Section: Introductionmentioning

confidence: 99%

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

Chetoui,

Akhloufi

2024

Fire

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Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry.

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Section: Introductionmentioning

confidence: 99%

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

Chetoui,

Akhloufi

2024

Fire

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“…Deep learning-based smoke detection (including forest fire smoke) has been widely studied and applied in the field. Compared to smoke classification [21][22][23][24][25][26] and smoke detection tasks [27][28][29][30][31][32][33], smoke segmentation not only detects the presence of smoke and its location but also provides information about the approximate smoke area and boundary contour [2,3,[34][35][36][37][38][39]. This additional information can be valuable in assessing the scale of the fire and predicting its potential spread.…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion

Han,

Tian,

Zheng

et al. 2024

Forests

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The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained.

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“…Whose classical algorithms are YOLO (you only look once) series [14] and SSD (single-shot multibox detector) algorithm [15]. In addition, the re-searchers have proposed an improved CNN-based method for smoke and fire detection [16][17][18]. Recently, researchers start to use Transformer [19] Backbone network to improve neural networks, whose typical ones are ViT (vision transformer) [20], Swin [21] and PVT (pyramid vision transformer) [22].…”

Section: Introductionmentioning

confidence: 99%

Lightweight forest smoke and fire detection algorithm based on improved YOLOv5

Yang,

Zhu,

Sun

et al. 2023

PLoS ONE

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Smoke and fire detection technology is a key technology for automatically realizing forest monitoring and forest fire warning. One of the most popular algorithms for object detection tasks is YOLOv5. However, it suffers from some challenges, such as high computational load and limited detection performance. This paper proposes a high-performance lightweight network model for detecting forest smoke and fire based on YOLOv5 to overcome these problems. C3Ghost and Ghost modules are introduced into the Backbone and Neck network to achieve the purpose of reducing network parameters and improving the feature’s expressing performance. Coordinate Attention (CA) module is introduced into the Backbone network to highlight the object’s important information about smoke and fire and to suppress irrelevant background information. In Neck network part, in order to distinguish the importance of different features in feature fusing process, the weight parameter of feature fusion is added which is based on PAN (path aggregation network) structure, which is named PAN-weight. Multiple sets of controlled experiments were conducted to confirm the proposed method’s performance. Compared with YOLOv5s, the proposed method reduced the model size and FLOPs by 44.75% and 47.46% respectively, while increased precision and mAP(mean average precision)@0.5 by 2.53% and 1.16% respectively. The experimental results demonstrated the usefulness and superiority of the proposed method. The core code and dataset required for the experiment are saved in this article at https://github.com/vinchole/zzzccc.git.

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A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires

Cited by 10 publications

References 31 publications

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion

Lightweight forest smoke and fire detection algorithm based on improved YOLOv5

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