YOLOv11-CHBG: A Lightweight Fire Detection Model

Fire is a disaster that seriously threatens people’s lives. Because fires occur suddenly and spread quickly, especially in densely populated places or areas where it is difficult to evacuate quickly, it often causes major property damage and seriously endangers personal safety. Therefore, it is necessary to detect the occurrence of fires accurately and promptly and issue early warnings. This study introduces YOLOv11-CHBG, a novel detection model designed to identify flames and smoke. On the basis of YOLOv11, the C3K2-HFERB module is used in the backbone part, the BiAdaGLSA module is proposed in the neck, the SEAM attention mechanism is added to the model detection head, and the proposed model is more lightweight, offering potential support for fire rescue efforts. The model developed in this study is shown by the experimental results to achieve an average precision (mAP@0.5) of 78.4% on the Dfire datasets, with a 30.8% reduction in parameters compared to YOLOv11. The model achieves a lightweight design, enhancing its significance for real-time fire and smoke detection, and it provides a research basis for detecting fires earlier, preventing the spread of fires and reducing the harm caused by fires.