融合注意力机制的无锚点森林火灾检测算法

森林火灾、野火是一个重大的自然灾害问题，每年全球各地植被都会受到严重的破坏。为了提高森林火灾的防控精度，针对传统方法具有火灾背景复杂、准确率低、效率低等问题，本文提出一种基于CenterNet的森林火灾检测算法。CenterNet作为一种无锚的方法，将目标定义为一个点，通过关键点估计定位目标的中心点，可以有效避免小目标的漏检。同时基于高效深层特征提取网络ResNet50，融合ECA模块以抑制无用信息，增加模型的特征提取能力。在公开森林火灾数据集上进行实验表明，与其他算法相比，本文提出的森林火灾检测算法误检率低，识别精度达到92.39%，F1值为0.86，Recall值为79.75%，FPS为43.31。本文提出的方法检测精度高，可满足实时检测森林火灾和实施精准施救的要求。

An Anchor-free Forest Fire Detection Algorithm Incorporating Attention Mechanism

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.