Fire Detection for CCTV surveillance system using YOLOv2
Demo for CCTV surveillance system using Deep Learning, typically YOLOv2 network training demo.
Key Objective for this demo
- Applying deep learning to Video streams from CCTV
- YOLOv2 deep learning model implemented to detect fire from video stream
Demo development Workflow
- Large dataset access : imagedatastore
- Labeling data : Automatic fire labeling class for image labeler defined using image processing apps, e.g. color thresholder, image segmenter
- Training : YOLOv2 training using feature extraction layers + yolov2 layers
- Deployment : Inference speed acceleration by generating CUDA mex file for real-time prediction
Dataset Used
- Cazzolato, Mirela T., et al. "FiSmo: A Compilation of Datasets from Emergency Situations for Fire and Smoke Analysis." Proceedings of the satellite events (2017).
Copyright 2019 The MathWorks, Inc.
Citation pour cette source
Wanbin Song (2024). Fire Detection for CCTV surveillance system using YOLOv2 (https://github.com/wanbin-song/FireDetectionYOLOv2), GitHub. Récupéré le .
Compatibilité avec les versions de MATLAB
Plateformes compatibles
Windows macOS LinuxCatégories
- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation > Object Detection Using Features > Face Detection >
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Version | Publié le | Notes de version | |
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1.0.0.1 | Connected to Github |
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1.0.0 |