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.
Zitieren als
Wanbin Song (2024). Fire Detection for CCTV surveillance system using YOLOv2 (https://github.com/wanbin-song/FireDetectionYOLOv2), GitHub. Abgerufen .
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
Windows macOS LinuxKategorien
- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation > Object Detection Using Features > Face Detection >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Live Editor erkunden
Erstellen Sie Skripte mit Code, Ausgabe und formatiertem Text in einem einzigen ausführbaren Dokument.
Versionen, die den GitHub-Standardzweig verwenden, können nicht heruntergeladen werden
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
1.0.0.1 | Connected to Github |
|
|
1.0.0 |