Efficient attention based deep fusion CNN for smoke detection in fog environment

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 selfcreated 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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a smoke detection method combining attention mechanism, feature-level fusion, and decision-level fusion module is proposed, with a new fog smoke dataset established. The proposed method outperforms existing ones in terms of detection accuracy, precision, recall, and F1 score.