Abstract: Forest fire and wildfire are major natural disaster problems, and vegetation is severely damaged all over the world every year. In order to improve the accuracy of forest fire prevention and control, aiming at the problems of complex fire background, low accuracy and low efficiency of traditional methods, this paper proposes a forest fire detection algorithm based on CenterNet. As an anchorless method, CenterNet defines a target as a point and locates the centroid of the target by key point estimation, which can effectively avoid the missed detection of small targets. At the same time, based on an efficient deep feature extraction network, ResNet50, it incorporates an ECA module to suppress useless information and increase the feature extraction capability of the model.Experiments conducted on public forest fire datasets show that compared with other arithmetic methods, the forest fire detection algorithm proposed in this paper has a low false detection rate and a recognition accuracy of 92.39% with a F1 value of 0.86, a Recall value of 79.75%, and a FPS of 43.31.The proposed method has a high detection accuracy and achieves real-time detection of forest fires and implementation of accurate rescue.

Key words: attention mechanism, anchorless detection, forest fire