Channel‐wise attention model‐based fire and rating level detection in video. Issue 2 (31st May 2019)

• Record Type: Journal Article
• Authors: Wu, Yirui; He, Yuechao; Shivakumara, Palaiahnakote; Li, Ziming; Guo, Hongxin; Lu, Tong
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 2(2019)
• Page Start: 117
• Page End: 121
• Publication Date: 2019-05-31
• Subjects: feature extraction -- learning (artificial intelligence) -- neural nets; channel‐wise attention model‐based fire -- rating -- natural disaster -- global warning -- unexpected fire -- deep‐learning models -- rescue team -- hybrid deep convolutional neural network -- motion detection -- maximally stable extremal region -- channel‐wise attention mechanism -- deep neural network -- fire level; C5260B Computer vision and image processing techniques -- C5290 Neural computing techniques -- C6170K Knowledge engineering techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0022
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Due to natural disaster and global warning, one can expect unexpected fire, which causes panic among people and extent to death. To reduce the impact of fire, the authors propose a new method for predicting and rating fire in video through deep‐learning models in this work such that rescue team can save lives of people. The proposed method explores a hybrid deep convolutional neural network, which involves motion detection and maximally stable extremal region for detecting and rating fire in video. Further, the authors propose to use a channel‐wise attention mechanism of the deep neural network for detecting rating of fire level. Experimental results on a large dataset show the proposed method outperforms the existing methods for detecting and rating fire in video.