Method and system for smog recognition based on LBP Gaussian pyramid

Abstract

The present invention discloses a method and a system for smog recognition based on a LBP (Local Binary Patterns) Gaussian pyramid. Suspected smog area images are subjected to graying and then twice Gaussian smoothing and sampling to obtain grey-scale maps with 1 / 4 and 1 / 16 sizes, and the grey-scale maps with 1 / 4 and 1 / 16 sizes are combined with an original image grey-scale maps to form three layers of pyramid images; LBP operators of P being equal to 8 and R being equal to 1 are employed for the three layers of Gaussian pyramid grey-scale maps to calculate and obtain binary system LBP codes of the three layers of Gaussian pyramid grey-scale maps, a rotation invariant mode and an equivalent mode are employed to perform dimension reduction of each layer of LBP codes, nine types of LBP codemodes are obtained, and statistics of the number of each type of LBP codes are employed to take the number of each type of LBP codes as one feature value; and AdaBoost input vectors are formed by employing 27 feature values of the three layers of LBP Gaussian pyramids for discrimination of smog and false smog interference. The method provided by the invention has good robustness and high recognition rate.

Description

technical field [0001] The invention relates to the technical field of image processing and image classification, in particular to a smoke recognition method and system based on an LBP Gaussian pyramid. Background technique [0002] Severe fire accidents often lead to serious casualties and huge property losses. If the fire can be detected and called in time at the initial stage of the fire, the loss can be minimized. Therefore, early detection of fire is very important. Under normal circumstances, "smoke is the beginning of fire", and smoke is the accompanying product of the second stage of fire. If the fire smoke can be detected in time, fire rescue measures can be taken as soon as possible to reduce personal casualties and property losses. [0003] LBP (local binary patterns) is an effective texture description operator. It is a binary description that expresses the size relationship between a certain pixel of a grayscale image and surrounding pixels. It has the advantag...

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Problems solved by technology

However, its application range is limited to the area where the detector is installed, and the installation and maintenance costs of the device are relatively high

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