Details

Autor(en) / Beteiligte

• Kang, Li-Wei
• Wang, I-Shan
• Chou, Ke-Lin
• Chen, Shih-Yu
• Chang, Chuan-Yu

Titel

Image-Based Real-Time Fire Detection using Deep Learning with Data Augmentation for Vision-Based Surveillance Applications

Ist Teil von

• 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.