A survey on vision-based outdoor smoke detection techniques for environmental safety

Chaturvedi, Shubhangi; Khanna, Pritee; Ojha, Aparajita

doi:10.1016/j.isprsjprs.2022.01.013

Cited by 52 publications

(15 citation statements)

References 139 publications

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“…The smoke detection methods based on color representation are susceptible to brightness and are poorly robust to changes in the environment ( Chaturvedi et al., 2022 ). In recent years, more and more researchers have been using different characterization methods for smoke detection of fires.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised wildfire smoke detection based on smoke-aware consistency

Wang

Grau²,

Guerra³

et al. 2022

Front. Plant Sci.

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The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

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Section: Related Workmentioning

confidence: 99%

Semi-supervised wildfire smoke detection based on smoke-aware consistency

Wang

Grau²,

Guerra³

et al. 2022

Front. Plant Sci.

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“…As mentioned above, a large part of smoke and fire detection algorithms have been developed for fixed surveillance cameras; an extensive study of such solutions can be found in the article [ 4 ]. The majority of such methods use motion detection as a first step.…”

Section: Introductionmentioning

confidence: 99%

“…It is challenging to find works in the literature that effectively solve the problem of smoke and fire detection with classical rule-based methods or using classical machine learning techniques; most solutions use deep learning approaches [ 4 , 18 , 22 ]. Traditional methods based on feature extraction and color transformation can be effective for fire detection [ 23 ], but their application to smoke is much more problematic.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles

Szczepański

2023

Sensors

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The paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for detecting smoke objects over buildings using stationary video sequences acquired with a drone in hover with the camera in the nadir position is proposed. The method uses differential frame information from stabilized video sequences and the YOLOv7 classifier. A convolutional network classifier was used to detect the roofs of buildings, using a custom training set adapted to the type of data used. Such a solution, although quite effective, is not very practical for the end user, but it enables the automatic generation of a comprehensive training set for classifiers based on deep neural networks. The effectiveness of such a solution was tested for the latest version of the YOLOv7 classifier. The tests proved the effectiveness of the described method, both for single images and video sequences. In addition, the obtained classifier correctly recognizes objects for sequences that do not meet some of the initial assumptions, such as the angle of the camera capturing the image.

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“…The most noticeable quality of the smoke is its color. For this reason, much emphasis has been placed on different color spaces for smoke detection [ 20 , 21 ].…”

Section: Introductionmentioning

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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A survey on vision-based outdoor smoke detection techniques for environmental safety

Cited by 52 publications

References 139 publications

Semi-supervised wildfire smoke detection based on smoke-aware consistency

Semi-supervised wildfire smoke detection based on smoke-aware consistency

Vision-Based Detection of Low-Emission Sources in Suburban Areas Using Unmanned Aerial Vehicles

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

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