A Yolo-based Approach for Fire and Smoke Detection in IoT Surveillance Systems

Fire and smoke detection in IoT surveillance systems is of utmost importance for ensuring public safety and preventing property damage. While traditional methods have been used for fire detection, deep learning-based approaches have gained significant attention due to their ability to learn complex patterns and achieve high accuracy. This paper addresses the current research challenge of achieving high accuracy rates with deep learning-based fire detection methods while keeping computation costs low. This paper proposes a method based on the Yolov8 algorithm that effectively tackles this challenge through model generation using a custom dataset and the model's training, validation, and testing. The model's efficacy is succinctly assessed by the precision, recall and F1curve metrics, with notable proficiency in fire detection, crucial for early warnings and prevention. Experimental results and performance evaluations show that our proposed method outperforms other state-of-the-art methods. This makes it a promising fire and smoke detection approach in IoT surveillance systems.