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Fire smoke detection algorithm based on motion characteristic and convolutional neural networks

机译：基于运动特征和卷积神经网络的火烟检测算法

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摘要

It is a challenging task to recognize smoke from visual scenes due to large variations in the feature of color, texture, shapes, etc. The current detection algorithms are mainly based on single feature or fusion of multiple static features of smoke, which leads to low detection accuracy. To solve this problem, this paper proposes a smoke detection algorithm based on the motion characteristics of smoke and the convolutional neural networks (CNN). Firstly, a moving object detection algorithm based on background dynamic update and dark channel priori is proposed to detect the suspected smoke regions. Then, the features of suspected region is extracted automatically by CNN, on that the smoke identification is performed. Compared to previous work, our algorithm improves the detection accuracy, which can reach 99% in the testing sets. For the problem that the region of smoke is relatively small in the early stage of smoke generation, the strategy of implicit enlarging the suspected regions is proposed, which improves the timeliness of smoke detection. In addition a fine-tuning method is proposed to solve the problem of scarce of data in the training network. Also, the algorithm has good smoke detection performance by testing under various video scenes.
机译：由于颜色，纹理，形状等特征的巨大变化，从视觉场景中识别烟雾是一项艰巨的任务。当前的检测算法主要基于烟雾的单个特征或多个静态特征的融合，从而导致烟雾浓度低。检测精度。为了解决这个问题，本文提出了一种基于烟雾运动特性和卷积神经网络（CNN）的烟雾检测算法。首先，提出了一种基于背景动态更新和暗通道先验的运动目标检测算法，以检测可疑烟雾区域。然后，通过CNN自动提取可疑区域的特征，然后执行烟雾识别。与以前的工作相比，我们的算法提高了检测精度，在测试集中可以达到99％。针对烟雾产生初期烟雾区域相对较小的问题，提出了隐含扩大可疑区域的策略，提高了烟雾检测的及时性。此外，提出了一种微调方法来解决训练网络中数据的稀缺问题。此外，通过在各种视频场景下进行测试，该算法具有良好的烟雾检测性能。

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