A Real-Time Video Flame Detection Algorithm Based on Improved Yolo-v3

ZHAO Yuanyuan; ZHU Jun; XIE Yakun; LI Weilian; GUO Yukun

doi:10.13203/j.whugis20190440

Volume 46 Issue 3

Mar. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(3): 326-334. > DOI: 10.13203/j.whugis20190440

ZHAO Yuanyuan, ZHU Jun, XIE Yakun, LI Weilian, GUO Yukun. A Real-Time Video Flame Detection Algorithm Based on Improved Yolo-v3[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 326-334. DOI: 10.13203/j.whugis20190440

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A Real-Time Video Flame Detection Algorithm Based on Improved Yolo-v3

1.
Faculty of Geosciences and Environment Engineering, Southwest Jiaotong University, Chengdu 611756, China

Funds:

National Natural Science Foundation of China 41871289

Scientific Research Project of Sichuan Provincial Department of Natural Resources KJ-2020-4

Sichuan Youth Science and Technology Innovation Team 2020JDTD0003

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Author Bio:
ZHAO Yuanyuan, postgraduate, specializes in virtual geographic environment and disaster scenario modeling. E-mail: 3011441848@qq.com
Corresponding author:
ZHU Jun, PhD, professor. E-mail: vgezj@163.com
Received Date: August 04, 2020
Published Date: March 04, 2021

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Abstract

Abstract

Objectives In order to solve the problems of the low accuracy and the slow speed of the existing video image flame detection methods, we propose a real-time video flame detection algorithm based on improved Yolo-v3 to achieve real-time and efficient detection of flames in the video.
Methods Firstly, in the feature extraction stage, the multi-scale detection network is improved. We add a new-scale feature and then improved the networks ability to learn the shallow information of the images by further integrating multi-scale features. Using this method, the accurate identification of small flame is achieved. Secondly, in the target detection stage, we use the improved K-means clustering algorithm to optimize the multi-scale prior frames, and make them adapt to the changing posture and shape of the flame. Finally, after detecting video flames based on improved Yolo-v3, we use the unique flicker characteristics of the flame to check the video again, and eliminate the false detection frame in the detection result. And in this method the detection accuracy is further improved.
Results In order to prove the effectiveness of our method, the video flames are detected from both accuracy and speed, and the results are compared with the advanced flame detection methods in recent years. The results show that the average accuracy rate of our method can reach 98.5%, the false detection rate is as low as 2.3%, and the average detection rate is 52 frames/s, so our method has better performance in terms of accuracy and speed.
Conclusions The effectiveness of this method is proved through multiple sets of experiments. Comparing with the existing flame detection methods, our method can be more effectively applied to video flame detection.
- flame detection,
- video image,
- Yolo-v3,
- flicker characteristics,
- multi-scale detection

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