Yangın ve duman algılama tabanlı yapay zeka teknikleri

An efficient monitoring system is required for accurate fire and smoke detection to stop the fire and guarantee the safety of the occupants' lives. This calls into question the existing sophisticated capabilities of fire alarm systems and highlights the need for a thorough smoke and fire detection system. It's critical to detect smoke and fire. Given the time and accuracy needed for fire detection, convolutional (deep) neural networks have been developed to recognize objects, even though flames usually inflict significant damage. YOLO was applied, and a suggested method called YOLOv8 was also created. Still, much research has been done on using real data with deep learning. The suggested method was to use an image-rich Smoke and Fire database. The findings show that the suggested approach performs better than others in accuracy, model size, and detection speed. Consequently, we demonstrated how to apply the YOLOv8l algorithm as quickly as possible to identify important fire and smoke properties; identification is faster and more accurate than earlier methods; the YOLOv8 algorithm achieves an average @mAP of 96.6%. Keywords: Artificial Intelligence, Convolutional Neural Network, Deep Learning, Image Processing, Fire and Smoke Detection, YOLOv8