Deep Learning for Fire and Smoke Detection in Outdoor Spaces

2021 ◽  
pp. 195-209
Author(s):  
Margarita N. Favorskaya ◽  
Lakhmi C. Jain
Keyword(s):  
Deep Learning ◽  
Smoke Detection ◽  
Outdoor Spaces

scholarly journals False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1167 ◽  
Author(s):  
Yeunghak Lee ◽  
Jaechang Shim
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
Detection Performance ◽  
Economic Losses ◽  
Smoke Detection ◽  
Temporal Features ◽  
False Positive Detection ◽  
Surveillance Cameras ◽  
Fire Flame

Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.

scholarly journals A Study on Flame and Smoke Detection Algorithm Using Convolutional Neural Network Based on Deep Learning

2020 ◽  
Vol 20 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Jinkyu Ryu ◽  
Dongkurl Kwak
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Smoke Detection ◽  
Conventional Methods ◽  
Learning Data

Recently, cases of large-scale fires, such as those at Jecheon Sports Center in 2017 and Miryang Sejong Hospital in 2018, have been increasing. We require more advanced techniques than the existing approaches to better detect fires and avoid these situations. In this study, a procedure for the detection of fire in a region of interest in an image is presented using image pre-processing and the application of a convolutional neural network based on deep-learning. Data training based on the haze dataset is included in the process so that the generation of indoor haze smoke, which is difficult to recognize using conventional methods, is also detected along with flames and smoke. The results indicated that fires in images can be identified with an accuracy of 92.3% and a precision of 93.5%.

Spatio-temporal deep learning fire smoke detection

2021 ◽  
Vol 36 (8) ◽  
pp. 1186-1195
Author(s):  
Fan WU ◽  
◽  
Hui-qin WANG ◽  
Ke WANG
Keyword(s):  
Deep Learning ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Spatio Temporal
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