Paper Contents
Abstract
The prompt recognition of fires within industrial zones is of utmost importance in guaranteeing safety. Minimize harm and mitigate substantial financial damage. Conventional fire detection systems frequently experience delays in response times and exhibit elevated rates of false alarms. Particularly within intricate industrial settings. As per the data provided by India's National Crime Records Bureau (NCRB), it is projected that by the year 2020, 17,000 incidents of fires will be documented in industrial premises. Over 700 fires ignited, leading to substantial casualties and property damage. This study suggests employing an AI-enhanced surveillance system that utilizes the YOLOv8 object recognition model to improve fire detection capabilities in industrial settings. The system integrates sophisticated computer vision methodologies and deep learning algorithms to detect fires. Prompt and timely notification are facilitated, allowing for early intervention based on a comprehensive dataset of fire and smoke images processed by the YOLOv8 model. The performance of the model is enhanced through data augmentation and optimization of hyperparameters. The system architecture has been devised to ensure scalability and adaptability across diverse industrial settings. It efficiently utilizes both hardware and software components. Assessment of the proposed system involves determining its accuracy, robustness, and processing speed in comparison to conventional methods. and demonstrates substantial enhancements. The YOLOv8 model demonstrates a notable level of accuracy and recall rates, mitigates false positives, and ensures dependable early warning systems. Real-world case studies also serve to validate the system's performance across a range of industrial scenarios in light of the fire incident. This research contributes to the progress of fire detection technology through the provision of a resilient and effective AI-based solution. With potential applications in a range of.Keywords: AI-powered surveillance, YOLOv8, fire detection, industrial safety, computer vision, deep learning, real-time object detection, smoke detection, early warning systems, fire safety technology
Copyright
Copyright © 2024 Vishwas Katiyar. This is an open access article distributed under the Creative Commons Attribution License.
