Visual Smoke Detection Based on Ensemble Deep CNNs

Abstract

Smoke usually refers to a visible aerosol produced by fuel combustion, sometimes commingled with carbon and sulfur particles because of incomplete burning. The long-term accumulation of smoke aerosols is an important factor in the formation of haze, which is an environmental concern in many countries. Developing methods of intelligent smoke detection in the air would be greatly beneficial for such applications as industrial safety monitoring and prevention of air pollution for the purpose of wholly replacing manual processes. In this paper, we concentrate on resolving this difficulty. Unlike existing deep learning frameworks which lack generalization ability because they were developed for specific data instances, our proposed model aggregates simple deep convolutional neural networks but attains excellent performance. In order to capture the different aspects of smoke, we define a set of feature maps that are fed to a set of subnetworks. Each subnetwork is independently trained to deliver good detection performance. A final output is obtained by selectively aggregating the subnetwork responses via majority voting. In the experiment we conducted on two newly established noisy smoke image datasets corrupted by compression, our proposed model achieves a very high consistency beyond 97% on average between detection results and human judgements, outperforming other state-of-the-art smoke detection algorithms based on deep learning.