FIgLib &amp; SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

Dewangan, Anshuman; Pande, Yash; Braun, Hans-Werner; Vernon, F. L.; Perez, Ismael; Altıntaş, Ilkay; Cottrell, Garrison W.; Nguyen, Manh Duc

doi:10.3390/rs14041007

Cited by 25 publications

(18 citation statements)

References 37 publications

(80 reference statements)

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“…To address these issues, we contribute SmokeSeg dataset. SmokeSeg consists of 6,144 real images (the raw smoke images are sourced from FigLib (Dewangan et al 2022). ), which has the largest number of real images with pixelwise annotations in any publicly available smoke segmentation dataset.…”

Section: Experiments Smokeseg Datasetmentioning

confidence: 99%

FoSp: Focus and Separation Network for Early Smoke Segmentation

Yao,

Zhao,

Peng

et al. 2024

AAAI

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Early smoke segmentation (ESS) enables the accurate identification of smoke sources, facilitating the prompt extinguishing of fires and preventing large-scale gas leaks. But ESS poses greater challenges than conventional object and regular smoke segmentation due to its small scale and transparent appearance, which can result in high miss detection rate and low precision. To address these issues, a Focus and Separation Network (FoSp) is proposed. We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate. Next, we propose a Separation module that separates smoke images into a pure smoke foreground and a smoke-free background, enhancing the contrast between smoke and background fundamentally, improving segmentation precision. Finally, a Domain Fusion module is developed to integrate the distinctive features of the two modules which can balance recall and precision to achieve high F_beta. Futhermore, to promote the development of ESS, we introduce a high-quality real-world dataset called SmokeSeg, which contains more small and transparent smoke than the existing datasets. Experimental results show that our model achieves the best performance on three available smoke segmentation datasets: SYN70K (mIoU: 83.00%), SMOKE5K (F_beta: 81.6%) and SmokeSeg (F_beta: 72.05%). The code can be found at https://github.com/LujianYao/FoSp.

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Section: Experiments Smokeseg Datasetmentioning

confidence: 99%

FoSp: Focus and Separation Network for Early Smoke Segmentation

Yao,

Zhao,

Peng

et al. 2024

AAAI

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“…Fire Smoke [33,34]: This dataset consists of 1048 images, including 430 images with fire scenes, 457 images with smoke scenes, and 161 images without fire scenes. The data can be downloaded from the following website: https://www.kaggle.com/datasets/ashutosh69/fire-and-smoke-dataset (accessed on 27 September 2023).…”

Section: Datasetsmentioning

confidence: 99%

Integrated Algorithm Based on Bidirectional Characteristics and Feature Selection for Fire Image Classification

Wang,

Zhao,

Tao

2023

Electronics

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In some fire classification task samples, it is especially important to learn and select limited features. Therefore, enhancing shallow characteristic learning and accurately reserving deep characteristics play a decisive role in the final fire classification task. In this paper, we propose an integrated algorithm based on bidirectional characteristics and feature selection for fire image classification called BCFS-Net. This algorithm is integrated from two modules, a bidirectional characteristics module and feature selection module; hence, it is called an integrated algorithm. The main process of this algorithm is as follows: First, we construct a bidirectional convolution module to obtain multiple sets of bidirectional traditional convolutions and dilated convolutions for the feature mining and learning shallow features. Then, we improve the Inception V3 module. By utilizing the bidirectional attention mechanism and Euclidean distance, feature points with greater correlation between the feature maps generated by convolutions in the Inception V3 module are selected. Next, we comprehensively consider and integrate feature points with richer semantic information from multiple dimensions. Finally, we use convolution to further learn the deep features and complete the final fire classification task. We validated the feasibility of our proposed algorithm in three sets of public fire datasets, and the overall accuracy value in the BoWFire dataset reached 88.9%. The overall accuracy in the outdoor fire dataset reached 96.96%. The overall accuracy value in the Fire Smoke dataset reached 81.66%.

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“…Some researchers have concentrated on wildfire detection through fires [ 52 , 53 ]. In contrast, other studies have focused on wildfire detection using smoke features [ 54 , 55 ], which appear to be more appropriate for early wildfire detection because the fire in its early scene could be concealed, particularly in overgrown forests [ 56 , 57 ]. Several recent studies have aimed to simultaneously detect smoke and fire.…”

Section: Related Workmentioning

confidence: 99%

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Mukhiddinov

Abdusalomov

2022

Sensors

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Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset.

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FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

Cited by 25 publications

References 37 publications

FoSp: Focus and Separation Network for Early Smoke Segmentation

FoSp: Focus and Separation Network for Early Smoke Segmentation

Integrated Algorithm Based on Bidirectional Characteristics and Feature Selection for Fire Image Classification

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

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