FireFighterX: Detecting and Preventing Forest Fires with an Artificial Intelligence-Enabled Autonomous Vehicle

Nihat Pamuk

© 2025 Nihat Pamuk, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International. (CC BY-NC 4.0).

Citation Information: SAR Journal. Volume 8, Issue 4, Pages 297-307, ISSN 2619-9955, https://doi.org/10.18421/SAR84-01, December 2025.

Received: 19 September 2025.
Revised: 03 November 2025.
Accepted: 10 November 2025.
Published: 27 December 2025.

Abstract:

This study presents the development of FireFighterX, an autonomous fire extinguishing vehicle equipped with Artificial Intelligence (AI)-powered image processing techniques. The system employs an ESP32-CAM module and a deep learning-based Convolutional Neural Network (CNN) model for real-time fire detection, while Arduino-controlled motors enable autonomous navigation and a water-spraying system for intervention. Experimental tests demonstrated a 94% detection accuracy, rapid response, and effective fire suppression. The CNN model, trained with the binary cross-entropy loss function and Adam Optimizer, achieved low error rates and high learning performance. With its low-cost, modular design and adaptability to diverse geographical conditions, FireFighterX represents a significant advancement in automation and efficiency for firefighting, emerging as a promising solution for integration into global disaster management strategies.

Keywords – Fire detection, ESP32-CAM, AI, CNN, arduino, image processing, autonomous vehicle.

                   

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