Fire Detection Model Based on the Yolov8 Model for the Industrial Field
Abdul Salam, M., badr, A., Elbohoty, A., Mohamed, E. (2025). Fire Detection Model Based on the Yolov8 Model for the Industrial Field. EKB Journal Management System, 1(1), 29-39. doi: 10.21608/ijaici.2025.337982.1003
Mustafa Abdul Salam; Alsayed badr; Aya Abdelhamed Elbohoty; Eman Mohamed. "Fire Detection Model Based on the Yolov8 Model for the Industrial Field". EKB Journal Management System, 1, 1, 2025, 29-39. doi: 10.21608/ijaici.2025.337982.1003
Abdul Salam, M., badr, A., Elbohoty, A., Mohamed, E. (2025). 'Fire Detection Model Based on the Yolov8 Model for the Industrial Field', EKB Journal Management System, 1(1), pp. 29-39. doi: 10.21608/ijaici.2025.337982.1003
Abdul Salam, M., badr, A., Elbohoty, A., Mohamed, E. Fire Detection Model Based on the Yolov8 Model for the Industrial Field. EKB Journal Management System, 2025; 1(1): 29-39. doi: 10.21608/ijaici.2025.337982.1003

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, Page 29-39  XML PDF (1.16 MB)
Document Type: Original Article
DOI: 10.21608/ijaici.2025.337982.1003
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Authors
Mustafa Abdul Salam email 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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