Early Detection of Fire using YOLOv8 and Computer Vision

In the field of fire safety, enhanced detection systems are crucial for mitigating hazards and facilitating rapid responses. This research presents a novel hybrid and layered fire detection system that integrates machine learning (ML) and computer vision (CV) methodologies with extensive categorization to enhance fire protection in office buildings. To accurately evaluate and detect fire incidents promptly, the system employs a multi-level approach. Empirical investigations demonstrate the system's efficacy in practical environments. It achieves remarkable accuracy rates in fire detection and intensity rating through rigorous testing and data-driven validation. The findings indicate a significant reduction in false alerts compared to conventional systems, thereby enhancing the solution's reliability and utility. Additionally, case studies conducted in diverse office settings underscore the system's resilience and adaptability. Comparative analyses with other fire detection technologies highlight the advantages of the hybrid strategy. By employing ML algorithms for flame intensity analysis, the proposed method outperforms existing methods by accurately identifying hazards and initiating timely actions. Leveraging collected data, researchers can uncover patterns, optimize response strategies, and enhance overall fire safety measures in office buildings. The hybrid and layered fire detection system's distinctive amalgamation of state-of-the-art technology, empirical research, and user-centric design principles positions it as a groundbreaking innovation with the ability to redefine fire safety protocols and safeguard lives.