Fire detection method based on convolutional neural network

Abstract

The invention discloses a fire detection method based on a convolutional neural network, and the method comprises the steps: constructing the convolutional neural network for training, enabling the input of the convolutional neural network to be a collected fire image, and enabling the output of the convolutional neural network to be a probability value of whether an input image is a fire image; the method comprises the steps of verifying whether an input to-be-predicted picture is a fire picture, performing detection in a superpixel detection mode, performing superpixel segmentation on the to-be-predicted picture, performing binary classification detection on pixel points in each segmented superpixel region, and obtaining the shape of a fire on the to-be-predicted picture according to a detection result of each grid. Compared with a traditional target detection and positioning method, the method has the advantages that target positioning is achieved without a labeled training set, andin the aspect of accuracy, fire binary classification detection has the highest 93% accuracy in pictures and is low in complexity.

Description

technical field [0001] The invention belongs to the field of machine vision applications, and in particular relates to a picture and video fire detection technology based on a convolutional neural network Background technique [0002] Fires often occur in various scenarios, and timely detection of whether there is a fire has important application value in real life. It is widely used in video surveillance, traffic monitoring, public safety, urban planning, and the construction of smart supermarkets, such as monitoring the security of an important meeting place to ensure the smooth progress of the meeting. [0003] As a commonly used means of public security prevention and control, computer vision is used to judge scene pictures and videos, and most of them currently use big data-based target detection methods. The above detection methods have certain limitations in the face of complex scenes, less data, and light occlusion, and cannot guarantee the accuracy and reliability ...

AI Technical Summary

Problems solved by technology

The above detection methods have certain limitations in the face of complex scenes, less data, and light occlusion, and cannot guarantee the accuracy and reliability of detection.

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