A convolutional neural network-based flame detection method in video sequence

作者：Zhong, Zhen; Wang, Minjuan; Shi, Yukun; Gao, Wanlin^*

来源：Signal, Image and Video Processing, 2018, 12(8): 1619-1627.

DOI：10.1007/s11760-018-1319-4

• Convolutional neural network
• Flame detection
• Feature maps
• Deep learning

Computer vision-based fire detection is one of the crucial tasks in modern surveillance system. In recent years, the convolutional neural network (CNN) has become an active topic because of its high accuracy recognition rate in a wide range of applications. How to reliably and effectively solve the problems of flame detection, however, has still been a challenging problem in practice. In this paper, we proposed a novel flame detection algorithm based on CNN in real time by processing the video data generated by an ordinary camera monitoring a scene. Firstly, to improve the efficiency of recognition, a candidate target area extraction algorithm is proposed for dealing with the suspected flame area. Secondly, the extracted feature maps of candidate areas are classified by the designed deep neural network model based on CNN. Finally, the corresponding alarm signal is obtained by the classification results. The experimental results show that the proposed method can effectively identify fire and achieve higher alarm rate in the homemade database. The proposed method can effectively realize the real-time performance of fire warning in practice.

• 出版日期2018-11
• 单位中国农业大学