A Deep Learning Approach to Reduce False Alarms for Optical Smoke Detectors. (September 2020)

• Record Type: Journal Article
• Authors: Liu, Ming; Zhou, Hongbin; Ren, Yupeng; Lu, Wei
• Is Part Of: Journal of physics. Volume 1631(2020)
• Publication Date: 2020-09
• Subjects: Optical smoke detector -- false alarms -- deep learning -- GAN; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1631/1/012032
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Optical smoke detectors (OSDs) are fire-fighting equipment used to detect fire by detecting smoke with scattering phenomenon. From the principle of OSDs we can see that they are vulnerable to false alarms caused by dust or water steam. To reduce false alarms and to make OSDs more reliable, we present a deep learning approach to train a classifier to distinguish fire event from non-fire ones based on time series data. The classifier is modelled with a 1-Dimension convolutional neural network, and generative adversarial network is used to augment and balance training data. Experiment shows that our classifier can reduce more than 50% false alarms caused by water steam while maintaining sensitivity for fire events.