Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis
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 detectio...
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Politeknik Elektronika Negeri Surabaya
2025-01-01
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| Series: | Emitter: International Journal of Engineering Technology |
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| Online Access: | http://emitter2.pens.ac.id/ojs/index.php/emitter/article/view/882 |
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| author | Norisza Dalila Ismail Rizauddin Ramli Mohd Nizam Ab Rahman |
| author_facet | Norisza Dalila Ismail Rizauddin Ramli Mohd Nizam Ab Rahman |
| author_sort | Norisza Dalila Ismail |
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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.
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| format | Article |
| id | doaj-art-8da953a9845f4d8896c460a4621e4c7c |
| institution | Kabale University |
| issn | 2355-391X 2443-1168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Politeknik Elektronika Negeri Surabaya |
| record_format | Article |
| series | Emitter: International Journal of Engineering Technology |
| spelling | doaj-art-8da953a9845f4d8896c460a4621e4c7c2025-01-21T11:11:08ZengPoliteknik Elektronika Negeri SurabayaEmitter: International Journal of Engineering Technology2355-391X2443-11682025-01-0112210.24003/emitter.v12i2.882Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative AnalysisNorisza Dalila Ismail0Rizauddin Ramli1Mohd Nizam Ab Rahman2Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaFaculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia 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. http://emitter2.pens.ac.id/ojs/index.php/emitter/article/view/882Convolutional neural networkDeep learningKitchen fire detectionPerformance metricsYOLO |
| spellingShingle | Norisza Dalila Ismail Rizauddin Ramli Mohd Nizam Ab Rahman Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis Emitter: International Journal of Engineering Technology Convolutional neural network Deep learning Kitchen fire detection Performance metrics YOLO |
| title | Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis |
| title_full | Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis |
| title_fullStr | Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis |
| title_full_unstemmed | Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis |
| title_short | Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis |
| title_sort | evaluating yolov5s and yolov8s for kitchen fire detection a comparative analysis |
| topic | Convolutional neural network Deep learning Kitchen fire detection Performance metrics YOLO |
| url | http://emitter2.pens.ac.id/ojs/index.php/emitter/article/view/882 |
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