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.

烟雾通常是指燃料燃烧产生的可见气溶胶，有时由于燃烧不完全而与碳和硫颗粒混合。烟雾气溶胶的长期积累是形成雾霾的重要因素，雾霾是许多国家关注的环境问题。开发空气中的智能烟雾检测方法，将大大有利于工业安全监测和空气污染防治等应用，以完全取代人工流程。在本文中，我们专注于解决这个难题。与现有的深度学习框架因为它们是为特定数据实例而开发而缺乏泛化能力不同，我们提出的模型聚合了简单的深度卷积神经网络，但获得了出色的性能。为了捕捉烟雾的不同方面，我们定义了一组特征图，这些特征图被馈送到一组子网络。每个子网络都经过独立训练，以提供良好的检测性能。最终输出是通过多数投票有选择地聚合子网络响应来获得的。在我们对两个新建立的被压缩损坏的噪声烟雾图像数据集进行的实验中，我们提出的模型在检测结果和人类判断之间实现了平均超过 97% 的非常高的一致性，优于其他基于最新技术的烟雾检测算法关于深度学习。最终输出是通过多数投票有选择地聚合子网络响应来获得的。在我们对两个被压缩损坏的新建立的噪声烟雾图像数据集进行的实验中，我们提出的模型在检测结果和人类判断之间实现了超过 97% 的非常高的一致性，优于其他基于最新技术的烟雾检测算法关于深度学习。最终输出是通过多数投票有选择地聚合子网络响应来获得的。在我们对两个新建立的被压缩损坏的噪声烟雾图像数据集进行的实验中，我们提出的模型在检测结果和人类判断之间实现了平均超过 97% 的非常高的一致性，优于其他基于最新技术的烟雾检测算法关于深度学习。