Method for depth learn pattern recognition of fire image

Abstract

The invention discloses a fire image depth learning pattern recognition method, comprising the following steps: preprocessing a test sample and a training sample of the fire image; designing an improved GoogleNet-based deep learning network, which extracts features automatically through training to eliminate artificial traces; deep features are used to train the classifier and identify the fire test samples. The invention applies the image multi-scale convolution fusion network to the fire image pattern recognition technology, remarkably improves the training efficiency of the network, effectively solves the problems encountered in the fire recognition method, such as the accuracy and real-time property are not ideal, the network structure is complex, and the training time is long, the stability and the robustness are poor, and the like. The recognition accuracy of the trained network is 99.2% in three types of fire images.

Description

technical field [0001] The invention relates to the field of fire detection, in particular to a fire image deep learning pattern recognition method. Background technique [0002] When a fire breaks out, it will cause great harm to people's lives and property. If it can be found in the early stage of the fire and make an alarm in time, it will have important practical significance for reducing personal and property losses and gaining time for rescue. Collect fire data at the fire scene through sensors, cameras and other detection equipment, input the collected data into the fire recognition model, judge the occurrence of fire, and feed back the recognition results to the alarm control system to give an alarm to the fire situation. To achieve timely and accurate identification and alarm when it occurs, a reliable fire identification method with high identification accuracy is needed. [0003] Fire recognition is the core of the whole fire detection technology, and its recogni...

AI Technical Summary

Problems solved by technology

[0006] In summary, there is no effective solution to the problems of low accuracy, false positives, missed negatives, parameter redundancy and small sample overfitting in the existing technology of fire image recognition.

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