Fire Detection Model Based on the Yolov8 Model for the Industrial Field | ||
| International Journal of Applied Intelligent Computing and Informatics | ||
| Volume 1, Issue 1, May 2025, Pages 29-39 PDF (1.16 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijaici.2025.337982.1003 | ||
| Authors | ||
| Mustafa Abdul Salam* 1; Alsayed badr2; Aya Abdelhamed Elbohoty3; Eman Mohamed4 | ||
| 1Department of Artificial Intelligence, Benha University, Cairo, Egypt. | ||
| 2Scientific Computing Department, Benha University, Cairo, Egypt | ||
| 3Scientific Computing, benha university(Faculty of computers and artificial intelligence), zifta, egypt | ||
| 4Benha University | ||
| Abstract | ||
| This paper proposed a model to detect fires in the industrial field using You Only Look Once Version 8 (YOLOv8) framework. The proposed model is based on three primary stages which are data pre-processing, feature selection, and evaluating the results using a variety of metrics. Images are resized, enhanced, noise is reduced, and videos and images are labeled with bounding boxes surrounding the fires during the data pre-processing step. The YOLOv8 model's speed and accuracy make it the preferred choice for feature selection. The performance of the proposed model is assessed using a variety of metrics, including accuracy, precision, and recall. Furthermore, the suggested model is trained in a real-time system that is capable of processing camera feeds in real time. When a fire is detected, the building's fire alarm should go on to alert people and tell them to evacuate. The experiment's findings show that the recommended model produced results with 98.1% accuracy, 98.9% precision, 95.3% recall, and 98.1% mAP. Finally, the proposed model is contrasted with existing methods on the same dataset. يتم تغيير حجم الصور وتحسينها ، ويتم تقليل الضوضاء ، ويتم تصنيف مقاطع الفيديو والصور مع صناديق محيطة تحيط بالحرائق أثناء خطوة المعالجة المسبقة للبيانات. | ||
| Keywords | ||
| YOLOv8; deep learning; fire detection; Industrial Field | ||
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