An AI-Based Early Fire Detection System Utilizing HD Cameras and Real-Time Image Analysis

Abstract

In recent years, wildfires have become a major threat to human lives, property, and the environment. Timely response in the beginning phase of a fire is key to minimizing loss and risk. Conventional wildfire detection systems are based on bystanders reporting the fire, resulting in a delayed response time, during which the fire can spread out of control. This paper investigates the question: “Can AI object detection minimize the delay of wildfire detection and response?”. An early fire detection system is introduced, which utilizes state-of-the-art hardware-based on AI (artificial intelligence) object detection, and can be integrating it to emergency services to improve the response time for wildfires. DSPL is a proprietary technology Read Smart Sensor, short for DSPL (Dynamic Spatial sensor platform), a cloud based system working on high-definition panoramas of our sky for detection of objects within our space, utilizing solar powered energy & COMM infrastructure for on-time communication. Once activated, the AI model continues to analyze camera images every 60 seconds, detecting early signs of smoke that signal the likelihood of a fire and informing the fire department before it gets out of control. We describe the system architecture, the AI model framework, the training process, and the results obtained from testing and validation. In conclusion, the system proved to be effective in having the timely detection and reporting of fire incidents, by coordinating with responsive departments and minimizing the time required to mitigate a fire outbreak. With this, we have proved that AI object detection can be a powerful ally in the fight against wildfires, thus saving lives, assets, and biodiversity.

Received: 17 April 2023 | Revised: 9 June 2023 | Accepted: 12 June 2023

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Leendert Remmelzwaal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.