Abstract
Real-time smoke detection is of great significance for early warning of fire, which can avoid the serious loss caused by fire. Detecting smoke in actual scenes is still a challenging task due to large variance of smoke color, texture, and shapes. Moreover, the smoke detection in the actual scene is faced with the difficulties in data collection and insufficient smoke datasets, and the smoke morphology is susceptible to environmental influences. To improve the performance of smoke detection and solve the problem of too few datasets in real scenes, this paper proposes a model that combines a deep convolutional generative adversarial network and a convolutional neural network (DCG-CNN) to extract smoke features and detection. The vibe algorithm was used to collect smoke and nonsmoke images in the dynamic scene and deep convolutional generative adversarial network (DCGAN) used these images to generate images that are as realistic as possible. Besides, we designed an improved convolutional neural network (CNN) model for extracting smoke features and smoke detection. The experimental results show that the method has a good detection performance on the smoke generated in the actual scenes and effectively reduces the false alarm rate.
Highlights
In recent years, frequent fires have caused tremendous losses to human life and society
Results and Discussion of GAN and deep convolutional generative adversarial network (DCGAN) Models. e generative adversarial network (GAN) consists of two modules, the generator model (G) and the discriminator model (D). e best network performance is achieved by the two models fighting against each other
When the discriminator network distinguishes whether the output of the generator is a real sample or not, it can indicate what kind of sample is closer to the real sample through the gradient and adjust the generation network through this information. e function of the GAN is expressed as follows: x z min max
Summary
Frequent fires have caused tremendous losses to human life and society. Smoke is a characteristic of the initial stage of fire. If the smoke can be detected and alarmed as soon as possible, it can effectively reduce the incidence of fire. Erefore, it is of considerable significance to detect whether smoke is generated accurately. Traditional smoke detection methods are mostly based on sensors, and there is a strict requirement on the number of sensors and the location of installation. Sensors need to be installed near fire-prone places. Alarms will alert by detecting smoke particles in the air. E disadvantage is that it would take a long time to detect smoke. It will result in too long response time and the purpose of real-time monitoring will not be achieved
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