ISS Destiny Laboratory Smoke Detection Model

2007 ◽  
Author(s):  
John E. Brooker ◽  
David L. Urban ◽  
Gary A. Ruff
Keyword(s):  
Smoke Detection ◽  
Detection Model

scholarly journals Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1075
Author(s):  
Md Rashedul Islam ◽  
Md Amiruzzaman ◽  
Shahriar Nasim ◽  
Jungpil Shin
Keyword(s):  
Growth Analysis ◽  
Moving Objects ◽  
Feature Model ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Smoke Detection ◽  
Detection Model ◽  
Detection Systems ◽  
Feature Based ◽  
Growth Feature

This article concerns smoke detection in the early stages of a fire. Using the computer-aided system, the efficient and early detection of smoke may stop a massive fire incident. Without considering the multiple moving objects on background and smoke particles analysis (i.e., pattern recognition), smoke detection models show suboptimal performance. To address this, this paper proposes a hybrid smoke segmentation and an efficient symmetrical simulation model of dynamic smoke to extract a smoke growth feature based on temporal frames from a video. In this model, smoke is segmented from the multi-moving object on the complex background using the Gaussian’s Mixture Model (GMM) and HSV (hue-saturation-value) color segmentation to encounter the candidate smoke and non-smoke regions in the preprocessing stage. The preprocessed temporal frames with moving smoke are analyzed by the dynamic smoke growth analysis and spatial-temporal frame energy feature extraction model. In dynamic smoke growth analysis, the temporal frames are segmented in blocks and the smoke growth representations are formulated from corresponding blocks. Finally, the classifier was trained using the extracted features to classify and detect smoke using a Radial Basis Function (RBF) non-linear Gaussian kernel-based binary Support Vector Machine (SVM). For validating the proposed smoke detection model, multi-conditional video clips are used. The experimental results suggest that the proposed model outperforms state-of-the-art algorithms.

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.

scholarly journals Pixel level smoke detection model with deep neural network

2019 ◽  
Author(s):  
Muthukumaran Ramasubramanian ◽  
Aaron Kaulfus ◽  
Manil Maskey ◽  
Rahul Ramachandran ◽  
Iksha Gurung ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Smoke Detection ◽  
Detection Model

An Early Fire and Smoke Detection Model for Surveillance Systems Based on Dilated CNNs

2020 ◽  
Vol 11 (4) ◽  
pp. 51-66
Author(s):  
Yakhyokhuja Valikhujaev ◽  
◽  
Makhmudov Fazliddin ◽  
Muksimova Shakhnoza ◽  
YoungIm Cho
Keyword(s):  
Surveillance Systems ◽  
Smoke Detection ◽  
Detection Model

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Detection model of similar datasets

2019 ◽  
Author(s):  
Julio Acedo ◽  
Marcos Fernandez-Sellers ◽  
Adolfo Lozano-Tello
Keyword(s):  
Detection Model
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