Smoke detection based on video monitoring is of great importance for early fire warning. However, most of the smoke detection methods based on neural network only consider the normal weather. The harsh weather such as the fog environment is ignored. In this paper, we propose a smoke detection in normal and fog weather, which combines attention mechanism and feature-level and decision-level fusion module. First, a new fog smoke dataset with diverse positive and hard negative samples dataset is established through online collection and offline shooting. Then, an attention mechanism module combining spatial attention and channel attention is proposed to solve the problem of small smoke detection. Next, a lightweight feature-level and decision-level fusion module is proposed, which can not only improve the discrimination of smoke, fog and other similar objects, but also ensure the real-time performance of the model. Finally, a large number of comparative experiments on the existing dataset and our self-created dataset, show that our method can obtain higher detection accuracy rate, precision rate, recall rate, and F1 score from the perspective of overall, each category, small smoke and hard negative samples detection than the existing methods.

基于视频监控的烟雾探测对于早期火灾预警至关重要。但是，大多数基于神经网络的烟雾检测方法仅考虑正常天气。诸如雾霾之类的恶劣天气被忽略。在本文中，我们提出了一种在正常和大雾天气下的烟雾检测方法，该方法将注意机制与特征级和决策级融合模块相结合。首先，通过在线收集和离线拍摄，建立了一个新的雾烟数据集，其中包含正样本和负样本样本。然后，提出了一种将空间注意力和通道注意力相结合的注意力机制模块，以解决烟雾探测量小的问题。接下来，提出了一种轻量级的特征级和决策级融合模块，该模块不仅可以改善烟雾识别能力，雾和其他类似对象，也确保了模型的实时性能。最后，在现有数据集和我们自己创建的数据集上进行的大量对比实验表明，从总体，每个类别，小烟度的角度来看，我们的方法可以获得更高的检测准确率，准确率，召回率和F1得分。和硬阴性样品检测相比现有方法。