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Fire Detection for CCTV surveillance system using YOLOv2

version 1.0.0.1 (14.9 MB) by Wanbin Song
Fire Detection for CCTV surveillance system using YOLOv2

351 Downloads

Updated 26 Nov 2019

From GitHub

View license on GitHub

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.

Cite As

Wanbin Song (2021). Fire Detection for CCTV surveillance system using YOLOv2 (https://github.com/wanbin-song/FireDetectionYOLOv2), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019a
Compatible with R2019a and later releases
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.