Real-time video field fire smoke detection method based on convolutional neural network

Abstract

The invention provides a real-time video field fire smoke detection method based on a convolutional neural network. A smoke image data set is collected through an experimental simulation mode, and a training set, a test set and a verification set are created; the training set, the test set and the verification set are subjected to automatic annotation, and in combination of manual adjustment, thetraining set, the test set and the verification set with a real label are obtained respectively; the training set and the verification set with the real label are subjected to image rotation processing, color channel color addition and subtraction processing and scaling processing to obtain the processed training set and the processed verification set with the real label; the parameters of the convolutional neural network are initialized, and according to the training set with the real label after scaling processing, a well-built convolutional neural network model is trained; a to-be-detectedfield monitoring image is acquired in real time, and through the trained convolutional neural network model, a smoke target detection frame is predicted and optimized; and inter-frame confidence enhancement and relocation are carried out on the target detection result given by the trained convolutional neural network model.

Description

technical field [0001] The invention belongs to the technical field of smoke detection, in particular to a method for real-time detection of fire smoke in video field based on convolutional neural network. Background technique [0002] Wild fires occur from time to time due to natural temperature factors or human activities such as man-made straw burning. The location of the occurrence is hidden or the supervision area is too large. Once a fire occurs, it may cause huge economic losses. Early field fire detection relied on inspectors standing on high observation towers to monitor, but the heavy monitoring tasks and limited human energy made it sometimes impossible to detect fires in advance and make early warnings. At the same time, it was difficult to arrange traditional technologies such as temperature sensors in wild fires Therefore, in recent years, many scholars have shifted their attention from the flame target in the fire to the smoke target produced at the same time ...

AI Technical Summary

Problems solved by technology

However, the above methods still have the problem that the real-time and accuracy of smoke detection are difficult to balance.

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