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. 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. 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. 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.