A Multi-sensor Characteristic Parameter Fusion Analysis Based Electrical Fire Detection Model

2018 ◽  
pp. 397-410
Author(s):  
Xuewu Yang ◽  
Ke Zhang ◽  
Yi Chai ◽  
Yuan Li
Keyword(s):  
Fire Detection ◽  
Characteristic Parameter ◽  
Detection Model ◽  
Fusion Analysis

scholarly journals Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xi Cheng
Keyword(s):  
Search Algorithm ◽  
Fire Detection ◽  
Visual Task ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
Detection Model ◽  
Automatic Feature Extraction ◽  
Transfer Method ◽  
Research On Application

Most of the existing smoke detection methods are based on manual operation, which is difficult to meet the needs of fire monitoring. To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. The preselected area coordinates and the sample image of visual task are used as network learning. During the training process, we use the feature migration method to avoid the lack of smoke data or limited data sources. Finally, a target detection model is obtained, which is strongly related to a specified visual task, and it has well-trained weight parameters. Experimental results show that this method not only improves the detection accuracy but also effectively reduces the false alarm rate. It can not only meet the real time and accuracy of fire detection but also realize effective fire detection. Compared with similar fire detection algorithms, the improved algorithm proposed in this paper has better robustness to fire detection and has better performance in accuracy and speed.

Application Model Analysis on Automatic System of Fire Detection, Alarming and Extinguishment in Crude Oil Storage Tank

2014 ◽  
Vol 687-691 ◽  
pp. 890-894
Author(s):  
Xiao Dong Song ◽  
Long Zhe Jin
Keyword(s):  
Crude Oil ◽  
Storage Tank ◽  
Fault Tolerant ◽  
Fire Detection ◽  
Automatic Monitoring ◽  
Training Module ◽  
Detection Model ◽  
Supervised Training ◽  
Oil Storage ◽  
Oil Storage Tank

In order to realize functions of automatic monitoring, alarming and extinguishment on fire in crude oil storage tank, the paper came up with a fire detection model with multisensor, including selective module with fire feature combination, supervised training module and fire detection module. By regarding PNN as a classifier to carry out tests on effectiveness of the model, the conclusions that the model can reduce the influence of fire parameters’ fluctuation on detection results was drew. Moreover, an excellent fault-tolerant ability was possessed at the same time. Through some confirmatory experiments, The phenomenon was reached that two kinds of parameters in adoptive four parameters have no normal fire signal, but the model still can greatly distinguish from correct fire state.

Detection Model of Eggs Based on Improved BP Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 564-566
Author(s):  
Jian Yang ◽  
Chun Yan Xia ◽  
Zhan Wu Peng
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Experimental Verification ◽  
Bp Neural Network ◽  
Central Area ◽  
Characteristic Parameter ◽  
Mean Square ◽  
Neural Network Ensemble ◽  
Detection Model ◽  
The Mean

In order to improve the precision in the detection of egg fresh degree, an improved BP neural network ensemble method was proposed herein, where the K-means clustering was applied to optimize better neural network individuals and Lagrange multiplier was used to not only compute the weight of neural network individuals, forecast egg fresh degree (Haff value). Based on the image of eggs acquired by the machine vision device, taking color characteristic parameter (H, S and I) of the central area of the egg as input and Haff value of the egg as output, a model was constructed. With experimental verification, it was calculated that the mean square error of Haff value was 1.3764 and the generalization ability of the network was high.

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