Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection. (16th November 2020)

• Record Type: Journal Article
• Authors: Yin, Hang; Wei, Yurong; Liu, Hedan; Liu, Shuangyin; Liu, Chuanyun; Gao, Yacui
• Other Names: Yang Zhile Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-11-16
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/6843869
• Languages: English
• ISSNs: 1076-2787

Abstract

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.