TY - GEN
T1 - Image-based real-time fire detection using deep learning with data augmentation for vision-based surveillance applications
AU - Kang, Li Wei
AU - Wang, I. Shan
AU - Chou, Ke Lin
AU - Chen, Shih Yu
AU - Chang, Chuan Yu
N1 - Funding Information:
This work was supported in part by Ministry of Science and Technology (MOST), Taiwan, under the Grants MOST 108-2221-E-003-027-MY3 and MOST 105-2628-E-224-001-MY3. This work was also financially supported by the “Artificial Intelligence Recognition Industry Service Research Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85076361589&partnerID=8YFLogxK
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U2 - 10.1109/AVSS.2019.8909899
DO - 10.1109/AVSS.2019.8909899
M3 - Conference contribution
AN - SCOPUS:85076361589
T3 - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
BT - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
Y2 - 18 September 2019 through 21 September 2019
ER -