A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

Information Fusion, Journal Year: 2024, Volume and Issue: 108, P. 102369 - 102369

Published: March 22, 2024

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses underscored urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, use Artificial Intelligence (AI) wildfires, propelled by integration Unmanned Aerial Vehicles (UAVs) deep learning models, has created an unprecedented momentum implement develop more effective Although survey papers explored learning-based approaches wildfire, drone disaster management, risk assessment, a comprehensive review emphasizing application AI-enabled UAV systems investigating role methods throughout overall workflow multi-stage including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire fire growth modeling), post-fire tasks evacuation planning) is notably lacking. This synthesizes integrates state-of-the-science reviews research at nexus observations modeling, AI, UAVs - topics forefront advances elucidating AI performing monitoring actuation from pre-fire, through stage, To this aim, we provide extensive analysis remote sensing with particular focus on advancements, device specifications, sensor technologies relevant We also examine management approaches, monitoring, prevention strategies, well planning, damage operation strategies. Additionally, summarize wide range computer vision emphasis Machine Learning (ML), Reinforcement (RL), Deep (DL) algorithms for classification, segmentation, detection, tasks. Ultimately, underscore substantial advancement modeling cutting-edge UAV-based data, providing novel insights enhanced predictive capabilities understand dynamic behavior.

Language: Английский

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Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7

Buildings, Journal Year: 2023, Volume and Issue: 13(4), P. 1070 - 1070

Published: April 18, 2023

Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants smaller such as passenger cars. This incompatibility leads to reduced system performance undermines safety comfort. To address this issue, authors considered pedestrian crosswalks where vehicles, micro-mobility collide at right angles an urban road network. These sections areas people encounter perpendicularly. In order prevent accidents these areas, it is planned introduce a warning for pedestrians. procedure consists multi-stage activities by sending warnings drivers, disabled individuals, pedestrians phone addiction simultaneously. collective autonomy expected reduce number drastically. The aim paper automatic detection crosswalk network, designed from both vehicle perspectives. Faster R-CNN (R101-FPN X101-FPN) YOLOv7 network models were used analytical process dataset collected authors. Based on comparison between models, accuracy was 98.6%, while 98.29%. For different types crossings, gave better prediction results than R-CNN, although quite similar obtained.

Language: Английский

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YOLO-SF: YOLO for Fire Segmentation Detection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 111079 - 111092

Published: Jan. 1, 2023

Owing to the problems of missed detection, false and low accuracy current fire detection algorithm, a segmentation YOLO-SF, is proposed. This algorithm combines instance technology with YOLOv7-Tiny object improve its accuracy. We gather images that include both non-fire elements create dataset (FSD). The head YOLOR adopted model enhance ability express details. MobileViTv2 module introduced build backbone network, which effectively reduces parameters while ensuring network's extract features. Efficient Layer Aggregation Network (ELAN) neck network augmented Convolutional Block Attention Module (CBAM) broaden receptive field attention image channel spatial information. Additionally, Varifocal Loss used address problem inaccurate positioning in edge areas images. Compared for Box Mask, precision increases by 5.9% 6.2%, recall 2.5% 3.3%, mAP 4% 6%. In addition, FPS reaches 55.64, satisfying requirements real-time detection. improved exhibits good generalization performance robustness.

Language: Английский

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SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 289, P. 111528 - 111528

Published: Feb. 15, 2024

Language: Английский

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Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0299058 - e0299058

Published: March 12, 2024

This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized aerial fires using images obtained from camera mounted on designed four-rotor Unmanned Aerial Vehicle (UAV). The object performance YOLOv8 and YOLOv5 was examined identifying fires, CNN-RCNN network constructed to classify as containing fire or not. Additionally, this classification approach compared with the classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, used hardware real-time detection. Also, ground station interface receive display fire-related data. Thus, access coordinate information provided targeted intervention in case fire. UAV autonomously monitored designated area captured continuously. Embedded deep algorithms Nano board enable detect within its operational area. methods produced following results: 96% accuracy classification, 89% YOLOv8n detection, YOLOv5n

Language: Английский

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CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM

Fire, Journal Year: 2024, Volume and Issue: 7(2), P. 54 - 54

Published: Feb. 12, 2024

In the context of large-scale fire areas and complex forest environments, task identifying subtle features aspects can pose a significant challenge for deep learning model. As result, to enhance model’s ability represent its precision in detection, this study initially introduces ConvNeXtV2 Conv2Former You Only Look Once version 7 (YOLOv7) algorithm, separately, then compares results with original YOLOv7 algorithm through experiments. After comprehensive comparison, proposed ConvNeXtV2-YOLOv7 based on exhibits superior performance detecting fires. Additionally, order further focus network crucial information fires minimize irrelevant background interference, efficient layer aggregation (ELAN) structure backbone is enhanced by adding four attention mechanisms: normalization-based module (NAM), simple mechanism (SimAM), global (GAM), convolutional block (CBAM). The experimental results, which demonstrate suitability ELAN combined CBAM lead proposal new method detection called CNTCB-YOLOv7. CNTCB-YOLOv7 outperforms an increase accuracy 2.39%, recall rate 0.73%, average (AP) 1.14%.

Language: Английский

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Visual fire detection using deep learning: A survey

Neurocomputing, Journal Year: 2024, Volume and Issue: 596, P. 127975 - 127975

Published: June 1, 2024

Language: Английский

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

Fire, Journal Year: 2024, Volume and Issue: 7(4), P. 135 - 135

Published: April 12, 2024

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

Language: Английский

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A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection

Fire, Journal Year: 2025, Volume and Issue: 8(1), P. 26 - 26

Published: Jan. 13, 2025

Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke fire. However, accurate of fire in forests is challenging due different factors such as shapes, changing light, similarity with other smoke-like elements clouds. This study explores recent YOLO (You Only Look Once) deep-learning object models YOLOv9, YOLOv10, YOLOv11 detecting forest environments. The evaluation focuses on key performance metrics, including precision, recall, F1-score, mean average precision (mAP), utilizes two benchmark datasets featuring diverse instances across findings highlight the effectiveness small version (YOLOv9t, YOLOv10n, YOLOv11n) tasks. Among these, YOLOv11n demonstrated highest performance, achieving a 0.845, recall 0.801, mAP@50 0.859, mAP@50-95 0.558. versions (YOLOv11n YOLOv11x) were evaluated compared against several studies that employed same datasets. results show YOLOv11x delivers promising variants models.

Language: Английский

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Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Sensors, Journal Year: 2023, Volume and Issue: 23(16), P. 7078 - 7078

Published: Aug. 10, 2023

Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on can have extensive and severe wide-ranging impacts matters such as the safety of crew, cargo, environment, finances, reputation, etc. Therefore, timely detection fires is essential for quick responses powerful mitigation. The study this research paper presents a fire technique based YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. architecture, with an E-ELAN (extended efficient layer aggregation network) its backbone, serves basis our system. Its enhanced feature fusion makes it superior to all predecessors. To train model, we collected 4622 images various ship scenarios performed data augmentation techniques rotation, horizontal vertical flips, scaling. Our through rigorous evaluation, showcases capabilities recognition improve maritime safety. proposed strategy successfully achieves accuracy 93% detecting minimize catastrophic incidents. Objects having visual similarities may lead false prediction by but be controlled expanding dataset. However, model utilized real-time detector challenging environments small-object detection. Advancements models hold potential enhance measures, exhibits potential. Experimental results proved method used protection monitoring port areas. Finally, compared performance those recently reported fire-detection approaches employing widely matrices test classification achieved.

Language: Английский

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