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In view of the fact that most of the existing fire detection methods rely on heat detectors and smoke detectors, but the detection of temperature detectors and smoke detectors has a certain hysteresis, and cannot accurately detect the initial fire problems in real time. , Constructed a large-scale multi-scene fire image dataset, and at the same time annotated the flame and smoke object of the image dataset, and proposed a fire detection algorithm with attention mechanism, which uses color analysis to detect the suspected areas of flames and smoke, and then pay attention to the suspected areas of flames and smoke object. By combining the feature extraction capabilities of the deep network, the fire object detection model is obtained; the experimental results show that this method achieve better fire detection tasks Compared with the mean average precision (mAP) of the fire detection model based on YOLOv3, the effect is improved by 5.9%, while meeting the needs of real-time detection.