传统的火焰检测方法大多基于火焰的物理信号手动设计火焰特征,根据其使用模式进行识别。这类方法容易被外部环境干扰,且手动设计的火焰特征泛化性不强,当火焰形态或场景变化剧烈时,会降低识别精度。针对这一问题,提出了一种基于区域全卷积网络(R-FCN)结合残差网络(ResNet)的深度学习方法对火焰进行检测。通过特征提取网络自动提取特征,利用R-FCN确定火焰位置,并使用ResNet对该位置的火焰进行二次分类,以进一步降低误报率。该方法实现了端到端自动获取火焰特征并进行相应检测的过程,省去了传统火焰特征提取的过程。本文方法在Bilkent大学的火焰视频数据集上平均识别精度达到98.25%。

The flame pattern is artificially designed by most of the traditional flame detection methods based on physical signal of the flame, and is identified according to the pattern recognition method. These methods are easy to be interfered by the external environment. Because the generalization of artificially designed flame feature is not strong, the recognition accuracy will reduce when the flame shape or scene changes violently. To solve this problem, a method of deep learning for detecting the flame based on the regional full convolution network (R-FCN) with the residual network (ResNet) is proposed. The feature is extracted automatically by the feature extraction network, and the flame position is determined by R-FCN, and it is secondary classified by ResNet for further reducing the false alarm rate. The proposed method, which eliminates the feature extraction process of the traditional flame, realizes end-to-end automatic acquisition of flame characteristics and performs corresponding detection processes. An average recognition accuracy reaches to 98.25% in the flame video data set of Bilkent University.