A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection
Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke and fire. However, accurate detection of smoke and fire in forests is challenging due to different factors suc...
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MDPI AG
2025-01-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/8/1/26 |
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| author | Eman H. Alkhammash |
| author_facet | Eman H. Alkhammash |
| author_sort | Eman H. Alkhammash |
| collection | DOAJ |
| description | Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke and fire. However, accurate detection of smoke and fire in forests is challenging due to different factors such as different smoke shapes, changing light, and similarity of smoke with other smoke-like elements such as clouds. This study explores recent YOLO (You Only Look Once) deep-learning object detection models YOLOv9, YOLOv10, and YOLOv11 for detecting smoke and fire in forest environments. The evaluation focuses on key performance metrics, including precision, recall, F1-score, and mean average precision (mAP), and utilizes two benchmark datasets featuring diverse instances of fire and smoke across different environments. The findings highlight the effectiveness of the small version models of YOLO (YOLOv9t, YOLOv10n, and YOLOv11n) in fire and smoke detection tasks. Among these, YOLOv11n demonstrated the highest performance, achieving a precision of 0.845, a recall of 0.801, a mAP@50 of 0.859, and a mAP@50-95 of 0.558. YOLOv11 versions (YOLOv11n and YOLOv11x) were evaluated and compared against several studies that employed the same datasets. The results show that YOLOv11x delivers promising performance compared to other YOLO variants and models. |
| format | Article |
| id | doaj-art-11c52a39399e41649b8e8d56a1b25c1f |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-11c52a39399e41649b8e8d56a1b25c1f2025-01-24T13:32:20ZengMDPI AGFire2571-62552025-01-01812610.3390/fire8010026A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire DetectionEman H. Alkhammash0Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaForest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke and fire. However, accurate detection of smoke and fire in forests is challenging due to different factors such as different smoke shapes, changing light, and similarity of smoke with other smoke-like elements such as clouds. This study explores recent YOLO (You Only Look Once) deep-learning object detection models YOLOv9, YOLOv10, and YOLOv11 for detecting smoke and fire in forest environments. The evaluation focuses on key performance metrics, including precision, recall, F1-score, and mean average precision (mAP), and utilizes two benchmark datasets featuring diverse instances of fire and smoke across different environments. The findings highlight the effectiveness of the small version models of YOLO (YOLOv9t, YOLOv10n, and YOLOv11n) in fire and smoke detection tasks. Among these, YOLOv11n demonstrated the highest performance, achieving a precision of 0.845, a recall of 0.801, a mAP@50 of 0.859, and a mAP@50-95 of 0.558. YOLOv11 versions (YOLOv11n and YOLOv11x) were evaluated and compared against several studies that employed the same datasets. The results show that YOLOv11x delivers promising performance compared to other YOLO variants and models.https://www.mdpi.com/2571-6255/8/1/26fire detectionforest fireYOLOv9YOLOv10YOLOv11 |
| spellingShingle | Eman H. Alkhammash A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection Fire fire detection forest fire YOLOv9 YOLOv10 YOLOv11 |
| title | A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection |
| title_full | A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection |
| title_fullStr | A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection |
| title_full_unstemmed | A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection |
| title_short | A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection |
| title_sort | comparative analysis of yolov9 yolov10 yolov11 for smoke and fire detection |
| topic | fire detection forest fire YOLOv9 YOLOv10 YOLOv11 |
| url | https://www.mdpi.com/2571-6255/8/1/26 |
| work_keys_str_mv | AT emanhalkhammash acomparativeanalysisofyolov9yolov10yolov11forsmokeandfiredetection AT emanhalkhammash comparativeanalysisofyolov9yolov10yolov11forsmokeandfiredetection |