Image-based real-time fire detection using deep learning with data augmentation for vision-based surveillance applications

Li Wei Kang*, I. Shan Wang, Ke Lin Chou, Shih Yu Chen, Chuan Yu Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

With recent advances in embedded processing capability, vision-based real-time fire detection has been enabled in surveillance devices. This paper presents an image-based fire detection framework based on deep learning. The key is to learn a fire detector relying on tiny-YOLO (You Only Look Once) v3 deep model. With the advantage of lightweight architecture of tiny-YOLOv3 and training data augmentation by some parameter adjusting, our fire detection model can achieve better detection accuracy in real-time with lower complexity in the training stage. Experimental results have verified the effectiveness of the proposed framework.

Original languageEnglish
Title of host publication2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109909
DOIs
Publication statusPublished - 2019 Sep
Event16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 - Taipei, Taiwan
Duration: 2019 Sep 182019 Sep 21

Publication series

Name2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019

Conference

Conference16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
Country/TerritoryTaiwan
CityTaipei
Period2019/09/182019/09/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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