YOLO-MFD: Object Detection for Multi-Scenario Fires
Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, re...
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MDPI AG
2025-07-01
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| author | Fuchuan Mo Shen Liu Sitong Wu Ruiyuan Chen Tiecheng Song |
| author_facet | Fuchuan Mo Shen Liu Sitong Wu Ruiyuan Chen Tiecheng Song |
| author_sort | Fuchuan Mo |
| collection | DOAJ |
| description | Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenario fire detection. Firstly, to resolve missed detection caused by deformation of smoke and flames, a Scale Adaptive Perception Module (SAPM) is proposed. Secondly, aiming at the suppression of significant fire features by complex backgrounds, a Feature Adaptive Weighting Module (FAWM) is introduced to enhance the feature representation of fire. Finally, considering the difficulty in detecting small flames, a fine-grained Small Object Feature Extraction Module (SOFEM) is developed. Additionally, given the scarcity of multi-scenario fire datasets, this paper constructs a Multi-scenario Fire Dataset (MFDB). Experimental results on MFDB demonstrate that the proposed YOLO-MFD achieves a good balance between effectiveness and efficiency, achieving good effective fire detection performance across various scenarios. |
| format | Article |
| id | doaj-art-93fda8157bb843ddb400f179dea36d89 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-93fda8157bb843ddb400f179dea36d892025-08-20T02:45:52ZengMDPI AGInformation2078-24892025-07-0116762010.3390/info16070620YOLO-MFD: Object Detection for Multi-Scenario FiresFuchuan Mo0Shen Liu1Sitong Wu2Ruiyuan Chen3Tiecheng Song4School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaFire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenario fire detection. Firstly, to resolve missed detection caused by deformation of smoke and flames, a Scale Adaptive Perception Module (SAPM) is proposed. Secondly, aiming at the suppression of significant fire features by complex backgrounds, a Feature Adaptive Weighting Module (FAWM) is introduced to enhance the feature representation of fire. Finally, considering the difficulty in detecting small flames, a fine-grained Small Object Feature Extraction Module (SOFEM) is developed. Additionally, given the scarcity of multi-scenario fire datasets, this paper constructs a Multi-scenario Fire Dataset (MFDB). Experimental results on MFDB demonstrate that the proposed YOLO-MFD achieves a good balance between effectiveness and efficiency, achieving good effective fire detection performance across various scenarios.https://www.mdpi.com/2078-2489/16/7/620firefeature extractionsmall objectYOLO-MFD |
| spellingShingle | Fuchuan Mo Shen Liu Sitong Wu Ruiyuan Chen Tiecheng Song YOLO-MFD: Object Detection for Multi-Scenario Fires Information fire feature extraction small object YOLO-MFD |
| title | YOLO-MFD: Object Detection for Multi-Scenario Fires |
| title_full | YOLO-MFD: Object Detection for Multi-Scenario Fires |
| title_fullStr | YOLO-MFD: Object Detection for Multi-Scenario Fires |
| title_full_unstemmed | YOLO-MFD: Object Detection for Multi-Scenario Fires |
| title_short | YOLO-MFD: Object Detection for Multi-Scenario Fires |
| title_sort | yolo mfd object detection for multi scenario fires |
| topic | fire feature extraction small object YOLO-MFD |
| url | https://www.mdpi.com/2078-2489/16/7/620 |
| work_keys_str_mv | AT fuchuanmo yolomfdobjectdetectionformultiscenariofires AT shenliu yolomfdobjectdetectionformultiscenariofires AT sitongwu yolomfdobjectdetectionformultiscenariofires AT ruiyuanchen yolomfdobjectdetectionformultiscenariofires AT tiechengsong yolomfdobjectdetectionformultiscenariofires |