Comprehensive flame detection method based on ultraviolet and deep neural networks

Abstract

The invention provides a comprehensive flame detection system based on an ultraviolet and deep neural network. The comprehensive flame detection system comprises an ultraviolet detector, a camera, a controller and an alarm device, the data output end of the ultraviolet detector is connected with the first data input end of the controller, the trigger end of the controller is connected with the trigger end of the camera, the data output end of the camera is connected with the second data input end of the controller, and the alarm output end of the controller is connected with the alarm input end of the alarm device; the controller judges whether the data collected by the ultraviolet detector meet the triggering condition or not according to the data collected by the ultraviolet detector, ifyes, the controller triggers the camera to collect the data, and the controller judges the environment condition according to the data collected by the camera and gives an alarm through the alarm device. According to the invention, fire spreading can be judged and alarmed.

Description

technical field [0001] The invention relates to the field of deep learning target detection, in particular to a comprehensive flame detection method based on ultraviolet and deep neural network. Background technique [0002] Fire has always played an important role in the process of social development, but it is also a double-edged sword. In order to reduce the impact of fire on life, property and social economy, and control the fire within a very small range, fire detection technology is extremely important. With the development of electronic technology, fire detection devices have also emerged. According to the principle of fire detection, fire detection devices can be divided into smoke-sensing, temperature-sensing, photo-sensing, and image-sensing fire detectors. [0003] 1. The temperature-sensitive fire detector is a sensor that responds to changes in the ambient temperature, which can convert the temperature signal in the environment into an electrical signal. A smo...

AI Technical Summary

Problems solved by technology

[0004]2. Generally speaking, temperature-sensitive fire detectors are more reliable than smoke detectors, and have lower environmental requirements; Suitable for use in places where smoke and dust may be generated

Image processing technology has some existing algorithms for fire detection, but most of them apply some fixed scenes, which makes it difficult to simultaneously improve the fire detection rate and reduce the false detection rate.

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