…
comment: The repository administrator has not yet configured an RDF license.
primary topic: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2
type: AcademicArticle
type: Article
abstract: Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection.
date: 2025-10
volume: 25
issue: 20
is part of: Sensors
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Texto)
has document: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Otro)
subject: Engineering

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Texto)
type: Document
has file: sensors-25-06419.pdf
has part: sensors-25-06419.pdf

Sensors
type: Collection
issn: 14248220

Gerardo Méndez Mezquita
type: Person
given name: Gerardo
family name: Méndez Mezquita

Debora L. Ramírez-Vargas
type: Person
given name: Debora L.
family name: Ramírez-Vargas

Isabel de la Torre Díez
type: Person
given name: Isabel de la Torre
family name: Díez

Dilshod Sharobiddinov
type: Person
given name: Dilshod
family name: Sharobiddinov

Hafeez Ur Rehman Siddiqui
type: Person
given name: Hafeez Ur Rehman
family name: Siddiqui

Adil Ali Saleem
type: Person
given name: Adil Ali
family name: Saleem

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Otro)
type: Document
is version of: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Texto)
is volatile version of: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Texto)
is index codes version of: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17862 Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2
format: text/html
primary topic: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2

Engineering
type: Concept
preferred label: Ingeniería @es