A Lightweight Deep Learning Model for Forest Fires Detection and Monitoring

Yandouzi, Mimoun; Idrissi, Idriss; Boukabous, Mohammed; Grari, Mounir; Moussaoui, Omar; Azizi, Mostafa; Ghoumid, Kamal; Elmiad, Aissa Kerkour

doi:10.1007/978-981-19-6223-3_71

Cited by 2 publications

(1 citation statement)

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“…They offer benefits like quick coverage of large areas, access to remote locations, low altitude and night flight capability, thermal imaging, and real-time information for effective response [2]. Drones complement sensor-based architectures by swiftly confirming potential fires detected through telemetry [12]. Additionally, drones provide a cost-effective and eco-friendly alternative to traditional surveillance methods, reducing the need for human intervention and minimizing environmental impact.…”

Section: Drones/unmanned Aerial Vehiclesmentioning

confidence: 99%

Comparative study of teachable machine for forest fire and smoke detection by drone

Grari,

Yandouzi,

Mohammed

et al. 2024

Bulletin EEI

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Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and containment, safeguarding human lives, wildlife, and preserving natural resources for future generations. Utilizing drones and deep learning (DL) algorithms can significantly enhance early fire detection and minimize their devastating impact. In this paper, we examine teachable machine, a Google tool for creating DL models. We compare the top model generated by teachable machine for fire and smoke detection to models obtained through transfer learning from established DL models in image recognition and computer vision (CV), such as VGG16, VGG19, MobileNet, MobileNetv2, and MobileNetv3. The results underscore the significance of employing the teachable machine model in specific fire and smoke detection scenarios.

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Section: Drones/unmanned Aerial Vehiclesmentioning

confidence: 99%