An Improved Algorithm Based on Convolutional Neural Network for Smoke Detection

Yin, Hongcheng; Wei, Yurong

doi:10.1109/iucc/dsci/smartcns.2019.00063

Cited by 7 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the recognition rate of smoke with large changes in shape is not high. Yin and Wei [15] used an improved algorithm based on cascading classification and deep convolutional neural network for smoke detection. Kaabi et al [16] used deep belief networks to classify smoke and nonsmoke images, but the size of each frame of the smoke image will affect the effect of smoke recognition.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%