Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis

Abstract

Accurate and timely detection of kitchen fires is crucial for enhancing safety and reducing potential damage. This paper discusses comparative analysis of two cutting-edge object detection models, YOLOv5s and YOLOv8s, focusing on each performance in the critical application of kitchen fire detection. The performance of these models is evaluated using five main key metrics including precision, F1 score, recall, mean Average Precision across various thresholds (mAP50-95) and mean Average Precision at 50 percent threshold (mAP50). Results indicate that YOLOv8s significantly outperforms YOLOv5s in several metrics. YOLOv8s achieves a recall of 0.814 and an mAP50 of 0.897, compared to YOLOv5s' recall of 0.704 and mAP50 of 0.783. Additionally, YOLOv8s attains an F1 score of 0.861 and an mAP50-95 of 0.465, whereas YOLOv5s records an F1 score of 0.826 and mAP50-95 of 0.342. However, YOLOv5s shows a higher precision of 0.952 compared to YOLOv8s' 0.914. This detailed evaluation underscores YOLOv8s as a more effective model for precise fire detection in kitchen settings, highlighting its potential for enhancing real-time fire safety systems. Additionally, by offering the future work of integration of sensors with latest YOLO involvement can further optimize efficiency and fast detection rate.

Keywords

• Convolutional neural network
• Deep learning
• Kitchen fire detection
• Performance metrics
• YOLO