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, 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.