Fire point detection method and apparatus, and storage medium

Abstract

The invention provides a fire point detection method and apparatus, and a storage medium. The method comprises the steps of obtaining multi-channel historical remote sensing image data; constructing afire point detection model based on a convolutional neural network, and training the fire point detection model by adopting the historical remote sensing image data to obtain a trained fire point detection model; acquiring multi-channel real-time remote sensing image data, inputting the real-time remote sensing image data into the trained fire point detection model, extracting feature informationof each channel, and detecting a fire point in combination with the feature information of each channel and the real-time remote sensing image data. According to the technical scheme, the fire pointis detected by combining multi-channel real-time remote sensing image data, the detection precision is greatly improved, and the detection efficiency is high.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a fire point detection method, device and storage medium. Background technique [0002] With the increasingly serious environmental problems such as global warming, the frequency of natural disasters such as forest fires is increasing, and the losses caused are becoming more and more serious. Due to the wild environment such as forests and grasslands, when a fire occurs, it is difficult to find the fire at the first time only by the inspection of the staff, and when a fire occurs in the wild environment such as forests and grasslands, the fire spreads quickly. If it cannot be extinguished quickly, causing Both the loss and the difficulty of fighting will increase significantly. [0003] In order to be able to deal with the fire in the early stage and prevent greater losses, it is necessary to monitor the field environment and quickly detect the fire point...

AI Technical Summary

Problems solved by technology

[0004] First, based on the threshold-based detection algorithm developed by the AVHRR (Advanced Very High Resolution Radiometer, very high-resolution scanning radiometer) system, false alarms are reduced through night detection, but the detection process has a low resolution and is only applicable to In forest areas, the scope of application is small;

[0005] Second, the detection algorithm developed for the data collected by GOES-11 (Geostationary Environmental Observation Satellite-11) and GOES-12 (Geostationary Environmental Observation Satellite-12) can distinguish land and water complexes by removing clouds through masking methods, but the recall A higher rate will introduce more false alarms;

[0006] Third, a threshold-based fire detection algorithm proposed based on MODIS (Moderate-resolution Imaging Spectroradiometer) is used to solve the problem of omissions caused by small forest deforestation and dense smoke covering fires, but the detection accuracy and low detection efficiency;

[0007] Fourth, the fire point is detected by detecting thermal anomalies such as biomass burning during the day and night, but there are few spectral bands involved, and the detection accuracy and detection efficiency are low

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