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A fire image recognition method based on depth learning

An image recognition and deep learning technology, applied in the field of fire image recognition based on deep learning, can solve the problems that the accuracy is difficult to meet the requirements, the detection effect is not ideal, and the features are not obvious enough, so as to improve the accuracy, be easy to extract, and speed up the simulation. effect of speed

Active Publication Date: 2019-03-26
XI AN JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

Compared with the dynamic features, the static features of the smoke are difficult to extract. The manual extraction of features is not only a heavy workload, but also the features are not obvious enough, the accuracy rate is difficult to meet the requirements, and the detection effect is not ideal.

Method used

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  • A fire image recognition method based on depth learning
  • A fire image recognition method based on depth learning
  • A fire image recognition method based on depth learning

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Experimental program
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Embodiment

[0096] The fire image recognition method based on deep learning of the present embodiment, comprises the steps:

[0097] Step 1: Construct neural network sample training set and test set:

[0098] In this example, the pictures containing the smoke in the early stage of the fire and the normal pictures without the fire are collected for training the convolutional neural network. Specifically, the pictures containing the smoke in the early stage of the fire were collected through fire video extraction and small-scale open flame experiment shooting, and the normal pictures without fire were collected through shooting in daily life. The training set has a total of 10,712 photos, including 2,201 smoke pictures, which are labeled as 1, and 8,501 non-smoking pictures, which are labeled as 0;

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Abstract

The invention belongs to the technical field of image information processing, and discloses a fire image recognition method based on depth learning. The method comprises the following steps: collecting smoke pictures and normal pictures in the early fire stage as a training set and a test set of a convolution neural network; Obtaining dark channel images of each image to compose a final training set and a test set; constructing Convolution neural model to determine whether smoke could be detected or not. Obtaining The smoke detection model by training the neural network, and testing the reddest pair model and evulating its performance. Compared with the prior art, the method of the invention improves the accuracy of smoke detection in a single image by utilizing dark channel images and depth learning, simultaneously improves the detection speed, and can be practically applied to the fire detection work of cities or forests.

Description

technical field [0001] The invention belongs to the technical field of image information processing, and in particular relates to a fire image recognition method based on deep learning. Background technique [0002] Fire detection has always been an important field of image information processing technology. How to apply image information processing technology to effectively control fire and prevent fire spread has attracted the attention of many researchers and has become one of the research hotspots in the field of computer vision. [0003] Generally speaking, the evolution of a fire can be divided into four stages: the invisible stage, the visible smoke stage, the open flame stage and the spreading stage. In order to minimize the loss caused by fire, fire early warning work is usually concentrated in the first two stages. Traditional fire detection mainly uses sensors such as temperature sensors, gas sensors, and humidity sensors to analyze parameters such as ambient tem...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G08B17/10
CPCG08B17/10G06V20/52G06F18/241G06F18/214
Inventor 吕娜史夏豪
Owner XI AN JIAOTONG UNIV
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