scholarly journals Forestwatch® wildfire smoke detection system: lessons learned from its two-year operational trial

2010 ◽  
Author(s):  
M. Lalkovič ◽  
J. Pajtíková
Keyword(s):  
Detection System ◽  
Lessons Learned ◽  
Smoke Detection ◽  
Wildfire Smoke

scholarly journals Integer arithmetic approximation of the hog algorithm used for pedestrian detection

2017 ◽  
Vol 14 (2) ◽  
pp. 329-346 ◽  
Author(s):  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Dubravko Culibrk ◽  
Darko Stefanovic ◽  
Bojan Lalic
Keyword(s):  
Classification Accuracy ◽  
System Performance ◽  
Detection System ◽  
Pedestrian Detection ◽  
Lessons Learned ◽  
People Detection ◽  
Algorithm Performance ◽  
Integer Arithmetic ◽  
Point Arithmetic ◽  
Angle Calculation

This paper presents the results of a study of the effects of integer (fixed-point) arithmetic implementation on classification accuracy of a popular open-source people detection system based on Histogram of Oriented Gradients. It is investigated how the system performance deviates from the reference algorithm performance as integer arithmetic is introduced with different bit-width in several critical parts of the system. In performed experiments, the effects of different bit-width integer arithmetic implementation for four key operations were separately considered: HoG descriptor magnitude calculation, HoG descriptor angle calculation, normalization and SVM classification. It is found that a 13-bit representation of variables is more than sufficient to accurately implement this system in integer arithmetic. The experiments in the paper are conducted for pedestrian detection and the methodology and the lessons learned from this study allow generalization of conclusions to a broader class of applications.

scholarly journals Computer Vision Application for Early Stage Smoke Detection on Ships

2016 ◽  
Vol 52 (1) ◽  
pp. 63-80
Author(s):  
Miroslav Bistrović ◽  
Jasmin Čelić ◽  
Domagoj Komorčec
Keyword(s):  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Water Mist ◽  
Smoke Detector ◽  
Smoke Detection ◽  
Engine Room ◽  
Computer Image Processing ◽  
Flame Detector ◽  
Safety Features

Nowadays, ship’s engine room is fire protected by automatic fire fighting systems, usually controlled from a place located outside the engine room. In order to activate the water mist extinguishing system automatically, at least two different fire detectors have to be activated. One of these detectors is a flame detector that is not hampered by various air flows caused by ventilation or draft and is rapidly activated and the other is smoke detector which is hampered by these flows causing its activation to be delayed. As a consequence, the automatic water mist extinguishing system is also delayed, allowing for fire expansion and its transfer to surrounding rooms. In addition to reliability of the ship’s fire detection system as one of the crucial safety features for the ship, cargo, crew and passengers, using a systematic approach in this research the emphasis is placed on the application of new methods in smoke detection such as the computer image processing and analysis, in order to achieve this goal. This paper describes the research carried out on board ship using the existing marine CCTV systems in early stages of smoke detection inside ship’s engine room, which could be seen as a significant contribution to accelerated suppression of unwanted consequences.

scholarly journals Lessons learned from the development of an affordable fall detection system for mHealth

2015 ◽  
Vol 1 (1) ◽  
pp. 59
Author(s):  
Miguel Angel Estudillo Valderrama ◽  
Laura M Roa Romero ◽  
Luis Javier Reina Tosina ◽  
Gerardo Barbarov Rostan ◽  
David Naranjo Hernandez
Keyword(s):  
Software Development ◽  
Mobile Health ◽  
Detection System ◽  
Fall Detection ◽  
Embedded Software ◽  
The State ◽  
Lessons Learned ◽  
Iterative Design ◽  
State Of Art ◽  
Industrial Prototype

This paper discusses some relevant methodological and implementation experiences acquired during the design and development of an embedded Fall Detection System (FDS), which can be of help in order to develop efficient and safe biomedical software for mobile Health (mHealth). For this purpose, an analysis of concepts like portability and iterative design, as well as some concerns about risks and safety involved, is provided in order to address some of the current challenges in embedded software, regarding the state-of-art of software development standards and mHealth technologies. This analysis is later evaluated for a custom pre-industrial prototype of the FDS, as an example of the feasibility of the approach followed. The results obtained show that a convenient methodological process can help to optimize available resources so as to provide affordable mHealth solutions.

scholarly journals Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Ching Ho
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Time Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Large Fires ◽  
Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

scholarly journals Early Smoke Detection on Video Using Wavelet Energy

2017 ◽  
Vol 2 (2) ◽  
pp. 79 ◽  
Author(s):  
Muhammad Zulfiqar Shafar ◽  
Tjokorda Agung Budi Wirayuda ◽  
Febryanti Sthevanie
Keyword(s):  
Spatial Analysis ◽  
Video Processing ◽  
Detection System ◽  
Color Space ◽  
Temporal Analysis ◽  
Processing Technique ◽  
Color Analysis ◽  
Smoke Detection ◽  
Wavelet Energy ◽  
Spatio Temporal

<p>Most of the smoke detection system these days still using sensors that have to receive specific particles before it could give a warning. But, this system takes some time to react and quite difficult to place in spacious room or the outdoor. To overcome this, there is some research that build smoke detection system using many kind video processing technique that could provide early warning. In this research, wavelet energy was used to detect smoke in the video.  To determine candidate blocks in a frame that contain smoke, this research performed background subtraction and color analysis based on HSV color space. Then implementing spatial analysis and spatio-temporal analysis by using wavelet energy method and accumulative motion orientation to detect the smoke. This system using combination of dataset from previous research [1], downloaded from various sources and self-made dataset. Based on testing process using those dataset, this system reaches 91.05% accuracy for block-level and 72.22% accuracy for frame-level.</p><strong>Keywords: </strong>Accumulative motion orientation, smoke detection, spatial analysis, spatio-temporal analysis, video processing, wavelet energy

scholarly journals Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5508
Author(s):  
Mira Jeong ◽  
MinJi Park ◽  
Jaeyeal Nam ◽  
Byoung Chul Ko
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Low Cost ◽  
Learning Technology ◽  
Smoke Detection ◽  
Short Term ◽  
Wildfire Smoke ◽  
Teacher Student ◽  
Long Short Term Memory ◽  
Spatial And Temporal Characteristics

As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.

Sign in / Sign up

Export Citation Format

Share Document