Wildfire Smoke Detection Research Articles

Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models

Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models

Attention-driven YOLOv5 for wildfire smoke detection from camera images

Attention-driven YOLOv5 for wildfire smoke detection from camera images

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

Improving Computer Vision-Based Wildfire Smoke Detection by Combining SE-ResNet with SVM

Wildfire is one of the most critical natural disasters that poses a serious threat to human lives as well as ecosystems. One issue hindering a high accuracy of computer vision-based wildfire detection is the potential for water mists and clouds to be marked as wildfire smoke due to the similar appearance in images, leading to an unacceptable high false alarm rate in real-world wildfire early warning cases. This paper proposes a novel hybrid wildfire smoke detection approach by combining the multi-layer ResNet architecture with SVM to extract the smoke image dynamic and static characteristics, respectively. The ResNet model is improved via the SE attention mechanism and fully convolutional network as SE-ResNet. A fusion decision procedure is proposed for wildfire early warning. The proposed detection method was tested on open datasets and achieved an accuracy of 98.99%. The comparisons with AlexNet, VGG-16, GoogleNet, SE-ResNet-50 and SVM further illustrate the improvements.

A wildfire smoke detection based on improved YOLOv8

A wildfire smoke detection based on improved YOLOv8

Wildfire Smoke Detection Based on Swin Transformer

Wildfires have devastating consequences for ecosystems and human lives, and it is crucial to detect wildfires accurately for reducing losses. Most researchers detected wildfire smoke by traditional smoke detection algorithms, deep learning algorithms, or a combination of both, but this still has a high rate of false positive. To enhance the accuracy of wildfire smoke detection, we present an advanced algorithm based on the Swin Transformer architecture, which improves remote dependence of high-order feature maps in the convolutional neural network, thus reducing the false positive rate of smoke detection by introducing the STB module to the GoogLeNet. We approach the task of wildfire smoke detection as a classification problem, where we divide the dataset of 18, 000 real wildfire smoke images into two distinct sets: a training set and a testing set. The experimental results support the efficacy and practicality of the proposed algorithm, exhibiting a remarkable smoke detection accuracy rate of 95% while successfully reducing the false alarm rate to as low as 4%. These findings underscore the algorithm’s capability to accurately identify wildfire smoke instances while minimizing erroneous detections.

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model's capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model's attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model's effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model's performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.

BCMNet: Cross-Layer Extraction Structure and Multiscale Downsampling Network With Bidirectional Transpose FPN for Fast Detection of Wildfire Smoke

At present, the wildfire smoke detection algorithm based on YOLOv3 has problems, such as low accuracy and slow detection speed. In this article, we propose a cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet) for fast detection of wildfire smoke. First, a cross-layer extraction module, which combines linear feature multiplexing and receptive field amplification, is designed. It can improve the speed and accuracy of wildfire smoke detection. Second, a multiscale downsampling module with different convolution kernels and maximum pooling operation is designed to preserve the details of the image while downsampling. Then, a bidirectional transposed FPN based on transposed convolution upsampling is designed. It can bidirectionally fuse visual features of shallow layer and semantic features of deep layer on the corresponding scale. The feature information flow between smoke feature maps of different resolution is emphasized. Finally, a wildfire smoke detection system of the Internet of Things based on BCMNet is built by combining the hardware and detection model. The experimental results show that the proposed method achieves 85.50% mAP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sup> and 79.98% mAP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</sup> at 40 FPS on NVIDIA Geforce RTX 2080 Ti, which is superior to the common smoke detection methods.

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

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.

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

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.

Camera-based wildfire smoke detection for foggy environments

Smoke is the first visible sign of forest fires and the most commonly used feature for early forest fire detection using data from cameras. However, one of the natural challenges is the dense fog that appears in forests, which decreases the detection accuracy or triggers false alarms. In this study, we propose a system with a deep neural network-based image preprocessing approach that significantly improves the smoke segmentation and classification performance by dehazing the camera view. Our experimental results provide that the classification models reach 99% F1 score for the correct classification of smoke when the image dehazing method is used before the training process. The smoke localization system achieves 60% average precision when the mask region-based convolutional neural network is used with the ResNet101-FPN backbone. The proposed approach can be utilized for all smoke segmentation frameworks to increase fire detection performance.

Nemo: An Open-Source Transformer-Supercharged Benchmark for Fine-Grained Wildfire Smoke Detection

Deep-learning (DL)-based object detection algorithms can greatly benefit the community at large in fighting fires, advancing climate intelligence, and reducing health complications caused by hazardous smoke particles. Existing DL-based techniques, which are mostly based on convolutional networks, have proven to be effective in wildfire detection. However, there is still room for improvement. First, existing methods tend to have some commercial aspects, with limited publicly available data and models. In addition, studies aiming at the detection of wildfires at the incipient stage are rare. Smoke columns at this stage tend to be small, shallow, and often far from view, with low visibility. This makes finding and labeling enough data to train an efficient deep learning model very challenging. Finally, the inherent locality of convolution operators limits their ability to model long-range correlations between objects in an image. Recently, encoder–decoder transformers have emerged as interesting solutions beyond natural language processing to help capture global dependencies via self- and inter-attention mechanisms. We propose Nemo: a set of evolving, free, and open-source datasets, processed in standard COCO format, and wildfire smoke and fine-grained smoke density detectors, for use by the research community. We adapt Facebook’s DEtection TRansformer (DETR) to wildfire detection, which results in a much simpler technique, where the detection does not rely on convolution filters and anchors. Nemo is the first open-source benchmark for wildfire smoke density detection and Transformer-based wildfire smoke detection tailored to the early incipient stage. Two popular object detection algorithms (Faster R-CNN and RetinaNet) are used as alternatives and baselines for extensive evaluation. Our results confirm the superior performance of the transformer-based method in wildfire smoke detection across different object sizes. Moreover, we tested our model with 95 video sequences of wildfire starts from the public HPWREN database. Our model detected 97.9% of the fires in the incipient stage and 80% within 5 min from the start. On average, our model detected wildfire smoke within 3.6 min from the start, outperforming the baselines.

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

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.

Wildfire Smoke Detection Based on Motion Aware and Feature Enhancement

Wildfire Smoke Detection Based on Motion Aware and Feature Enhancement

Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

Computer Vision-Based Wildfire Smoke Detection Using UAVs

This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection.

As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.

3D video semantic segmentation for wildfire smoke

Wildfires are a serious threat to ecosystems and human life. Usually, smoke is generated before the flame, and due to the diffusing nature of the smoke, we can detect smoke from a distance, so wildfire smoke detection is especially important for early warning systems. In this paper, we propose a 3D convolution-based encoder–decoder network architecture for video semantic segmentation in wildfire smoke scenes. In the encoder stage, we use 3D residual blocks to extract the spatiotemporal features of smoke. The downsampling feature from the encoder is upsampled by the decoder three times in succession. Then, three smoke map prediction modules are, respectively, passed, the output smoke prediction map is supervised by the binary image label, and finally, the final prediction is obtained by feature map fusion. Our model can achieve end-to-end training without pretraining from scratch. In addition, a dataset including 90 smoke videos is tested and trained in this paper. The experimental results of the smoke video show that our model quickly and accurately segmented the smoke area and produced few false positives.

Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

Smoke produced by wildfires is usually visible much earlier than flames. Hence, early detection of wildfire smoke is essential to prevent severe property losses and heavy casualties from catastrophic wildfires. Camera networks are being built and expanded to achieve timely wildfire smoke detection. To achieve the best camera coverage and detection accuracy with limited budget, an intelligent video smoke detection algorithm and an optimal wildfire camera placement strategy are in a critical need. In this paper, we propose an efficient video smoke detection framework designed for embedded applications on local cameras. It consists of two modules. In the first module, the original video frames are processed by local binary patterns and a dense optical flow estimator. In the second module, the produced features are then fed into a lightweight deep convolutional neural network, which serves as a binary classifier to detect the presence of smoke. We also formulate the wildfire camera placement problem as a binary integer programming problem to minimize the overall fire risk of a given area. Case studies on real-world videos are carried out to validate the accuracy as well as the computational and memory efficiency of the proposed smoke detection framework. We also validate our proposed camera placement strategy by simulating the deployment of wildfire cameras across a test region in Southern California.

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection and the imbalance of training data. To address these issues, we propose a novel framework integrating conventional methods into CNN for wildfire smoke detection, which consisted of a candidate smoke region segmentation strategy and an advanced network architecture, namely wildfire smoke dilated DenseNet (WSDD-Net). Candidate smoke region segmentation removed the complex backgrounds of the wildfire smoke images. The proposed WSDD-Net achieved multi-scale feature extraction by combining dilated convolutions with dense block. In order to solve the problem of the dataset imbalance, an improved cross entropy loss function, namely balanced cross entropy (BCE), was used instead of the original cross entropy loss function in the training process. The proposed WSDD-Net was evaluated according to two smoke datasets, i.e., WS and Yuan, and achieved a high AR (99.20%) and a low FAR (0.24%). The experimental results demonstrated that the proposed framework had better detection capabilities under different negative sample interferences.