Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study
Fires are classified into five types: A, B, C, D, and F/K, according to the components involved in combustion. Recognizing fire classes is critical, since each kind demands a unique suppression approach. Proper fire classification helps to decrease the risk to both life and property. The fuel type i...
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2025-01-01
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| author | Eman H. Alkhammash |
| author_facet | Eman H. Alkhammash |
| author_sort | Eman H. Alkhammash |
| collection | DOAJ |
| description | Fires are classified into five types: A, B, C, D, and F/K, according to the components involved in combustion. Recognizing fire classes is critical, since each kind demands a unique suppression approach. Proper fire classification helps to decrease the risk to both life and property. The fuel type is used to determine the fire class, so that the appropriate extinguishing agent can be selected. This study takes advantage of recent advances in deep learning, employing YOLOv11 variants (YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x) to classify fires according to their class, assisting in the selection of the correct fire extinguishers for effective fire control. Moreover, a hybrid model that combines YOLO11n and MobileNetV2 is developed for multi-class classification. The dataset used in this study is a combination of five existing public datasets with additional manually annotated images, to create a new dataset covering the five fire classes, which was then validated by a firefighting specialist. The hybrid model exhibits good performance across all classes, achieving particularly high precision, recall, and F1 scores. Its superior performance is especially reflected in the macro average, where it surpasses both YOLO11n and YOLO11m, making it an effective model for datasets with imbalanced classes, such as fire classes. The YOLO11 variants achieved high performance across all classes. YOLO11s exhibited high precision and recall for Class A and Class F, achieving an F1 score of 0.98 for Class A. YOLO11m also performed well, demonstrating strong results in Class A and No Fire with an F1 score of 0.98%. YOLO11n achieved 97% accuracy and excelled in No Fire, while also delivering good recall for Class A. YOLO11l showed excellent recall in challenging classes like Class F, attaining an F1 score of 0.97. YOLO11x, although slightly lower in overall accuracy of 96%, still maintained strong performance in Class A and No Fire, with F1 scores of 0.97 and 0.98, respectively. A similar study employing MobileNetV2 is compared to the hybrid model, and the results show that the hybrid model achieves higher accuracy. Overall, the results demonstrate the high accuracy of the hybrid model, highlighting the potential of the hybrid models and YOLO11n, YOLO11m, YOLO11s, and YOLO11l models for better classification of fire classes. We also discussed the potential of deep learning models, along with their limitations and challenges, particularly with limited datasets in the context of the classification of fire classes. |
| format | Article |
| id | doaj-art-2a211db83f964b6dab655bf99cd2d719 |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Fire |
| spelling | doaj-art-2a211db83f964b6dab655bf99cd2d7192025-01-24T13:32:18ZengMDPI AGFire2571-62552025-01-01811710.3390/fire8010017Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case StudyEman H. Alkhammash0Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFires are classified into five types: A, B, C, D, and F/K, according to the components involved in combustion. Recognizing fire classes is critical, since each kind demands a unique suppression approach. Proper fire classification helps to decrease the risk to both life and property. The fuel type is used to determine the fire class, so that the appropriate extinguishing agent can be selected. This study takes advantage of recent advances in deep learning, employing YOLOv11 variants (YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x) to classify fires according to their class, assisting in the selection of the correct fire extinguishers for effective fire control. Moreover, a hybrid model that combines YOLO11n and MobileNetV2 is developed for multi-class classification. The dataset used in this study is a combination of five existing public datasets with additional manually annotated images, to create a new dataset covering the five fire classes, which was then validated by a firefighting specialist. The hybrid model exhibits good performance across all classes, achieving particularly high precision, recall, and F1 scores. Its superior performance is especially reflected in the macro average, where it surpasses both YOLO11n and YOLO11m, making it an effective model for datasets with imbalanced classes, such as fire classes. The YOLO11 variants achieved high performance across all classes. YOLO11s exhibited high precision and recall for Class A and Class F, achieving an F1 score of 0.98 for Class A. YOLO11m also performed well, demonstrating strong results in Class A and No Fire with an F1 score of 0.98%. YOLO11n achieved 97% accuracy and excelled in No Fire, while also delivering good recall for Class A. YOLO11l showed excellent recall in challenging classes like Class F, attaining an F1 score of 0.97. YOLO11x, although slightly lower in overall accuracy of 96%, still maintained strong performance in Class A and No Fire, with F1 scores of 0.97 and 0.98, respectively. A similar study employing MobileNetV2 is compared to the hybrid model, and the results show that the hybrid model achieves higher accuracy. Overall, the results demonstrate the high accuracy of the hybrid model, highlighting the potential of the hybrid models and YOLO11n, YOLO11m, YOLO11s, and YOLO11l models for better classification of fire classes. We also discussed the potential of deep learning models, along with their limitations and challenges, particularly with limited datasets in the context of the classification of fire classes.https://www.mdpi.com/2571-6255/8/1/17YOLOv11classificationfire classesYOLOv8MobileNetdeep learning |
| spellingShingle | Eman H. Alkhammash Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study Fire YOLOv11 classification fire classes YOLOv8 MobileNet deep learning |
| title | Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study |
| title_full | Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study |
| title_fullStr | Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study |
| title_full_unstemmed | Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study |
| title_short | Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study |
| title_sort | multi classification using yolov11 and hybrid yolo11n mobilenet models a fire classes case study |
| topic | YOLOv11 classification fire classes YOLOv8 MobileNet deep learning |
| url | https://www.mdpi.com/2571-6255/8/1/17 |
| work_keys_str_mv | AT emanhalkhammash multiclassificationusingyolov11andhybridyolo11nmobilenetmodelsafireclassescasestudy |