Earticle

초록

영어

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

목차

Abstract
1. Introduction
2. Deep learning (Faster R-CNN)
2.1 Labeling dataset
2.2 Training dataset with Faster R-CNN
2.3 Creating inference graph
3. Structure of Similarity (SSIM)
4. Color histogram
5. Experimental results
6. Conclusion
Acknowledgement
References

키워드

저자

• Yeunghak Lee [ Department of Computer Engineering, Andong National University, Korea ]
• Jaechang Shim [ Department of Computer Engineering, Andong National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380