@article{uninipr17862,
           month = {Octubre},
           pages = {6419},
          author = {Dilshod Sharobiddinov and Hafeez Ur Rehman Siddiqui and Adil Ali Saleem and Gerardo M{\'e}ndez Mezquita and Debora L. Ram{\'i}rez-Vargas and Isabel de la Torre D{\'i}ez},
         journal = {Sensors},
          number = {20},
            year = {2025},
          volume = {25},
           title = {Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2},
             url = {http://repositorio.unib.org/id/eprint/17862/},
        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.}
}