eprintid: 17862 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/62 datestamp: 2025-10-27 09:19:14 lastmod: 2025-10-30 18:50:48 status_changed: 2025-10-27 09:19:14 type: article metadata_visibility: show creators_name: Sharobiddinov, Dilshod creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Saleem, Adil Ali creators_name: Méndez Mezquita, Gerardo creators_name: Ramírez-Vargas, Debora L. creators_name: Díez, Isabel de la Torre creators_id: creators_id: creators_id: creators_id: creators_id: debora.ramirez@unini.edu.mx creators_id: title: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2 ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: autonomous detection; edge computing; forest fire detection; MobileNetV2; real-time inference; smoke detection; wildfire monitoring 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 publication: Sensors volume: 25 number: 20 pagerange: 6419 id_number: doi:10.3390/s25206419 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s25206419 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Sharobiddinov, Dilshod; Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Méndez Mezquita, Gerardo; Ramírez-Vargas, Debora L. y Díez, Isabel de la Torre mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, debora.ramirez@unini.edu.mx, SIN ESPECIFICAR (2025) Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2. Sensors, 25 (20). p. 6419. ISSN 1424-8220 document_url: http://repositorio.unincol.edu.co/id/eprint/17862/1/sensors-25-06419.pdf