Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Web-Based Real-Time Wildfire Detection and Environmental Data Calibration using YOLOv8 Models

Authors
V. Sahaya Sakila1, *, S. Prasanth1, M. Vasanth Akash1, Kumari Ankita1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamilnadu, India
*Corresponding author. Email: Sahayasv2@srmist.edu.in
Corresponding Author
V. Sahaya Sakila
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_48How to use a DOI?
Keywords
Wildfire detection; YOLOv8; image processing; Streamlit; fire detection model; computer vision; object detection
Abstract

Early identification of wildfire outbreaks is crucial to minimize ecological damage and reduce the risk to human life and property. Conventional satellite-based detection techniques often lack the temporal resolution needed for rapid response. This study presents a cost-effective and deployable wildfire detection system that utilizes a computer vision approach with a YOLOv8 object detection model trained on the D-Fire image dataset. A user-interactive application is developed using Streamlit, enabling end users to detect fire and smoke in imagery either uploaded locally or accessed via URLs. The app is designed with flexibility in model selection and threshold configuration to balance detection precision and speed. The proposed system is evaluated based on prediction latency and detection performance across different YOLO model sizes, demonstrating effective results for image-based wildfire detection in varying environmental contexts. The lightweight and accessible design makes it suitable for integration into broader monitoring networks or as a rapid-deployment solution in remote or high-risk areas.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_48How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - V. Sahaya Sakila
AU  - S. Prasanth
AU  - M. Vasanth Akash
AU  - Kumari Ankita
PY  - 2025
DA  - 2025/10/31
TI  - Web-Based Real-Time Wildfire Detection and Environmental Data Calibration using YOLOv8 Models
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 576
EP  - 588
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_48
DO  - 10.2991/978-94-6463-866-0_48
ID  - Sakila2025
ER  -