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

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

5 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728109909
DOIs
出版狀態已發佈 - 2019 九月
事件16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 - Taipei, 臺灣
持續時間: 2019 九月 182019 九月 21

出版系列

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

會議

會議16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
國家/地區臺灣
城市Taipei
期間2019/09/182019/09/21

ASJC Scopus subject areas

  • 人工智慧
  • 電腦視覺和模式識別
  • 訊號處理

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