目的 与传统点式感烟器相比,图像烟雾检测具有响应速度快、非接触等显著优势,但烟雾形状、色彩、纹理千差万别,造成现有算法推广性能不好,亟需提高特征推广性能.为此提出了一种采用图像金字塔纹理和边缘多尺度特征的烟雾检测算法.方法 首先,该算法将图像进行金字塔分解,然后在每层图像上提取局部二元模式(LBP)和边缘方向直方图(EOH),采用不同池化方法得到金字塔局部二元模式(PLBP)和金字塔边缘方向直方图(PEOH)序列特征,分别用于表征烟雾纹理和边缘信息,首尾相连这些直方图后,采用支持向量机(SVM)进行训练、识别烟雾.结果 这金字塔纹理和边缘特征具有很好的分类性能,能够在比较大的图像库上达到94%以上的检测率和3.0%以下的误报率.结论 本文算法提取的纹理、边缘特征,对光照、尺度具有一定不变性,实验结果也表明本文特征对烟雾检测具有较好的推广性能.

Objective Image-based smoke detection methods have many advantages over traditional point-based smoke sensors, including their fast response and lack of contact. Nonetheless, existing methods remain challenged in terms of accurately detecting smoke in images due to significant variances in smoke shape, color, and texture. Method To improve recognition accuracy, we extract the features of pyramidal textures and edges to propose a novel image-based smoke detection method. We first decompose an image into an image pyramid and then extract the local binary patterns (LBPs) and edge orientation histograms (EOHs) from each layer of this pyramid. These patterns and histograms are called pyramidal LBPs (PLBPs) and pyramidal EOHs (PEOHs), respectively. We also adopt different pooling schemes to generate sequential PLBP and PEOH histograms that represent smoke textures and edges. Finally, we concatenate these histograms to form smoke feature vectors and use support vector machines for training and classification. Image pyramids contain scale information; thus, our pyramidal texture and edge features display certain scale-invariance. Result Experimental results show that the method reports detection rates of above 94% and false alarm rates of less than 3% given our large image datasets. Conclusion The texture and edge features extracted with our method exhibit certain illumination and scale invariances. Experiments indicate that these features discriminate and generalize effectively in terms of smoke detection.