Fire Detection for CCTV surveillance system using YOLOv2

버전 1.0.0.1 (14.9 MB) 작성자: Wanbin Song
Fire Detection for CCTV surveillance system using YOLOv2

다운로드 수: 521

업데이트 날짜: 2019/11/26

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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.

인용 양식

Wanbin Song (2022). Fire Detection for CCTV surveillance system using YOLOv2 (https://github.com/wanbin-song/FireDetectionYOLOv2), GitHub. 검색됨 .

MATLAB 릴리스 호환 정보
개발 환경: R2019a
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