Real-Time Fire Object Detection System Using Machine Learning

ITM Web of Conferences, Journal Year: 2025, Volume and Issue: 74, P. 01011 - 01011

Published: Jan. 1, 2025

The spread of forest fires presents one the major concerning ecosystems, human security, and property. This paper introduces a fire object detection system that employs machine learning algorithms to enhance early breakout response same. computer vision deep allow identify features related objects actions in images video feeds. set scenarios under various conditions, environmental backgrounds was curated for training CNN. In terms evaluating model’s robustness real applications across settings, metrics were defined by accuracy, precision, recall, F1 scores. proposed is designed alerting emergency responders within time so quicker intervention may be made possibly mitigate devastating effects wildfires. Future research will integration into real-time surveillance systems exploring added sensory data increase capabilities.

Language: Английский

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Advancements in Artificial Intelligence Applications for Forest Fire Prediction

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 704 - 704

Published: April 19, 2025

In recent years, the increasingly significant impacts of climate change and human activities on environment have led to more frequent occurrences extreme events such as forest fires. The recurrent wildfires pose severe threats ecological environments life safety. Consequently, fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing economic losses, improving management efficiency, ensuring personnel safety property security. To enhance comprehensive understanding wildfire research, this paper systematically reviews studies since 2015, focusing two key aspects: datasets with related tools algorithms. We categorized literature into three categories: statistical analysis physical models, traditional machine learning methods, deep approaches. Additionally, review summarizes data types open-source used in selected literature. further outlines challenges future directions, including exploring risk multimodal learning, investigating self-supervised model interpretability developing explainable integrating physics-informed models constructing digital twin technology real-time simulation scenario analysis. This study aims provide valuable support natural resource enhanced environmental protection through application remote sensing artificial intelligence

Language: Английский

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Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention

Fire, Journal Year: 2025, Volume and Issue: 8(5), P. 165 - 165

Published: April 22, 2025

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle meet the demands fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate identify smoke objects in visual images. However, research utilizing latest YOLO11 for remains sparse, addressing scale variability well practicality models continues be a focus. This study first compares classic YOLO series analyze its advantages tasks. Then, tackle challenges model practicality, we propose Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into create YOLO11s-MSCA. Experimental results show that outperforms other by balancing accuracy, speed, practicality. The YOLO11s-MSCA performs exceptionally on D-Fire dataset, improving overall accuracy 2.6% recognition 2.8%. demonstrates stronger ability small objects. Although remain handling occluded targets complex backgrounds, exhibits strong robustness generalization capabilities, maintaining performance complicated environments.

Language: Английский

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Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy

Drones, Journal Year: 2025, Volume and Issue: 9(5), P. 348 - 348

Published: May 3, 2025

This study aims to improve the accuracy of fire source detection, efficiency path planning, and precision firefighting operations in drone swarms during emergencies. It proposes an intelligent technology for based on multi-sensor integrated planning. The integrates You Only Look Once version 8 (YOLOv8) algorithm its optimization strategies enhance real-time detection capabilities. Additionally, this employs data fusion swarm cooperative path-planning techniques optimize deployment materials flight paths, thereby improving precision. First, a deformable convolution module is introduced into backbone network YOLOv8 enable flexibly adjust receptive field when processing targets, enhancing accuracy. Second, attention mechanism incorporated neck portion YOLOv8, which focuses feature regions, significantly reducing interference from background noise further recognition complex environments. Finally, new High Intersection over Union (HIoU) loss function proposed address challenge computing localization classification targets. dynamically adjusts weight various components training, achieving more precise classification. In terms visual sensors, infrared LiDAR sensors adopts Information Acquisition Optimizer (IAO) Catch Fish Optimization Algorithm (CFOA) plan paths coordinated swarms. By adjusting planning locations, can reach sources shortest possible time carry out operations. Experimental results demonstrate that improves by optimizing algorithm, algorithms, strategies. optimized achieved 94.6% small fires, with false rate reduced 5.4%. wind speed compensation strategy effectively mitigated impact material deployment. not only enhances but also enables rapid response scenarios, offering broad application prospects, particularly urban forest disaster rescue.

Language: Английский

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Drone-Based Wildfire Detection with Multi-Sensor Integration

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4651 - 4651

Published: Dec. 12, 2024

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire systems rely heavily on single-sensor approaches are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), novel deep learning-based model optimized drone-based wildfire smoke detection. AMSO-SFS combines optical, infrared, Synthetic Aperture Radar (SAR) data detect under varied visibility conditions. The introduces (SFS-Conv) module enhance the discriminative capacity of features in both spatial frequency domains. Furthermore, utilizes weakly supervised learning adaptive scale angle identify regions minimal labeled data. Extensive experiments show that proposed outperforms current state-of-the-art (SoTA) models, achieving robust performance while maintaining computational efficiency, it suitable real-time drone deployment.

Language: Английский

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