Forest Fire Identification in UAV Imagery Using X-MobileNet

Namburu, Anupama; Selvaraj, Prabha; Ragavanantham, Sumathi; Eldin, Elsayed Tag

doi:10.3390/electronics12030733

Cited by 35 publications

(16 citation statements)

References 45 publications

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“…Recently, the X-MobileNet was developed, with a key design focus on the efficient and swift detection of forest fires using UAV-sourced imagery. This model elegantly employs the scalable MobileNetV2 architecture and stands out due to its suitability for real-time deployment on drones, which often grapple with limited computational resources [58]. Another lightweight deep learning model has been developed, specifically tailored for wildfire detection using video cameras.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

Zhang,

Li,

Zhang

2023

Fire

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To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall rate, it achieved improvements in precision and average precision (AP) to varying degrees. The DenseM-YOLOv5 algorithm achieved a 2.24% increase in precision, as well as improvements of 1.2% in recall rate and 1.52% in AP compared to the YOLOv5 algorithm. Despite having a higher parameter size, the DenseM-YOLOv5 algorithm outperformed the SimAM-YOLOv5 algorithm in terms of precision and AP for forest fire detection.

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Section: Discussionmentioning

confidence: 99%

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

Zhang,

Li,

Zhang

2023

Fire

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“…Unmanned vehicles provide forestry enterprises with information about the space occupied by vegetation in a short time. The drone eliminates the need to detour a large area and provides genuine information to monitor ecosystem trends and possibly correct deviations [8][9].…”

Section: Fig 2 Software Model For Control Of Unmanned Aerial Vehicles...mentioning

confidence: 99%

Domestic experience of unmanned aerial vehicles for forest conditions monitoring

Shashkova,

Lukyanova,

Kazak

et al. 2024

BIO Web Conf.

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Russia has vast forest resources that require constant conservation and protection measures, the implementation of which is currently impossible without the use of aviation or satellites. The widespread introduction of unmanned aerial vehicles will make it possible to effectively monitor the condition and predict changes in forest lands. The article discusses standard methods for monitoring the condition of forests and their advantages and disadvantages, highlighting the main existing threats to forests. The classification of unmanned aerial vehicles is given. The advantages of using unmanned aerial vehicles in forestry are highlighted. A classification of the areas of application of unmanned aerial vehicles in forestry is given.

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“…UAVs can acquire information using on-board infrared automatic cameras or visual cameras. However, merging more UAVs results in enhanced fire monitoring services, such as a complementary view of wildfires, big fire coverage, and fire severity assessment [38]. UAV-based remote sensing is utilized in farming and forests enabling decision-making.…”

Section: Advanced Response Mechanism To Wildfire Using Drones and Iot...mentioning

confidence: 99%

A Synergistic Approach to Wildfire Prevention and Management using AI, Machine Learning, and 5G Technology in the United States

C. Stanley,

Alexander,

O. Austine

2024

Artificial Intelligence and Big Data

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In recent years, wildfires have emerged as a global environmental crisis, causing significant damage to ecosystems, and contributing to climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite advancements in detection methods, the increasing frequency of wildfires necessitates innovative solutions for early detection and efficient management. This study explores proactive approaches to detect and manage wildfires in the United States by leveraging Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. This would save a lot of lives and prevent huge economic loss that is attributed to wildfire outbreaks and spread. Advanced technology can be used in several ways to help in the proactive detection and management of wildfires. This includes the development of the use of AI-enabled remote sensing and signaling devices and leveraging 5G technology for active monitoring and mapping of wildfires. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.

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Forest Fire Identification in UAV Imagery Using X-MobileNet

Cited by 35 publications

References 45 publications

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

Domestic experience of unmanned aerial vehicles for forest conditions monitoring

A Synergistic Approach to Wildfire Prevention and Management using AI, Machine Learning, and 5G Technology in the United States

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