Early detection of fire and smoke using motion estimation algorithms utilizing machine learning

Abstract

An essential part of early warning and fire incident prevention in video surveillance systems is fire detection. The present study presents methodology that integrates motion estimation methods with the state-of-the-art convolutional neural network (CNN) architecture, YOLOv5, to provide effective fire detection. The methodology combines motion estimation techniques to improve the detection of dynamic changes suggestive of fire in video frames by the YOLOv5 model. The model incorporates motion analysis techniques, such as optical flow, to capture the spatial context and temporal relationships that are essential for differentiating between fire incidents and background activities. The research makes use of annotated datasets that cover a range of fire scenarios as well as non-fire activities, which guarantees reliable training and assessment of the YOLOv5 model. The outcomes of the experiments show how well the suggested strategy works to achieve high detection accuracy and real-time processing capabilities. Comprehensive performance indicators and comparison analysis are used to confirm the model’s ability to accurately pinpoint flames in the presence of changing ambient variables and motion dynamics. By utilizing YOLOv5 and motion estimation algorithms, this research advances the field of fire detection technologies and provides a scalable and effective solution that can be integrated into emergency response frameworks, smart cities, and surveillance systems. The results highlight the possibility for improved situational awareness and proactive fire management through the integration of CNN architectures with motion analysis techniques. This abstract highlights the improvements in accuracy and real-time applicability of YOLOv5 with motion estimation methods for fire detection, outlining the research emphasis, methodology, experimental validation, and possible consequences.