scholarly journals Flame Recognition in Video Images with Color and Dynamic Features of Flames

2019 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Jiaqing Chen ◽  
Xiaohui Mu ◽  
Yinglei Song ◽  
Menghong Yu ◽  
Bing Zhang
Keyword(s):  
Bp Neural Network ◽  
Color Model ◽  
False Alarms ◽  
Detection Accuracy ◽  
Dynamic Features ◽  
Network Testing ◽  
Video Images ◽  
Important Approach ◽  
Flame Detection ◽  
Hierarchical Features

Recently, video based flame detection has become an important approach for early detection of fire under complex circumstances. However, the detection accuracy of most existing methods remains unsatisfactory. In this paper, we develop a new algorithm that can significantly improve the accuracy of flame detection in video images. The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model. A few new dynamic and hierarchical features associated with the suspected regions, including the flicker frequency of flames, are then extracted and analyzed. The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a BP neural network. Testing results show that this algorithm is robust and efficient, and is able to significantly reduce the probability of false alarms.

A New Approach to Pipeline Leak Detection Using Electromagnetic Sensing

2016 ◽  
Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell
Keyword(s):  
Leak Detection ◽  
False Alarms ◽  
Detection Accuracy ◽  
Minimal Risk ◽  
Detection Systems ◽  
Acoustic Signatures ◽  
Cost System ◽  
Trade Offs ◽  
In Situ Sensors ◽  
Operational Range

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

2021 ◽  
pp. 1-14
Author(s):  
Hanqing Hu ◽  
Mehmed Kantardzic
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
False Alarms ◽  
Detection Accuracy ◽  
Real World Data ◽  
Traditional Concept ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Detection Algorithms ◽  
Concept Drift Detection

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

scholarly journals Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

2019 ◽  
Vol 11 (12) ◽  
pp. 3261 ◽  
Author(s):  
Jesus Olivares-Mercado ◽  
Karina Toscano-Medina ◽  
Gabriel Sánchez-Perez ◽  
Aldo Hernandez-Suarez ◽  
Hector Perez-Meana ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Fire Detection ◽  
Candidate Region ◽  
Support Vector ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multi Layer Perceptron ◽  
System Efficiency ◽  
Dynamic Texture ◽  
Early Fire Detection

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.

An Effective Flame Segmentation Method Based on Ohta Color Space

2012 ◽  
Vol 485 ◽  
pp. 7-11
Author(s):  
Jian Sheng Wu ◽  
Bin Zhang ◽  
Yun Ling Gao
Keyword(s):  
Detection System ◽  
Color Space ◽  
Weather Conditions ◽  
Color Model ◽  
Segmentation Method ◽  
Video Streams ◽  
Color Information ◽  
Otsu Method ◽  
Flame Detection ◽  
Rgb Color Space

A new fire segmentation method is proposed, which based on OHTA color model and Otsu method. Through this method we can accurately split flame in different weather conditions and different environmental conditions outdoor. The flame can be extracted completely. The method takes advantage of the flame image color space, color information and spatial characteristics of the different complementary color and provides a new idea for the extraction of flame image. This is an efficient flame segmentation algorithm, and time complexity is low. And the conversion from the RGB color space to OHTA color space is linear. It can achieve flame object segmentation from video streams in Video-based flame detection system

scholarly journals A New Multiple Hypothesis Tracker Integrated with Detection Processing

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5278 ◽  
Author(s):  
Wang ◽  
Sun ◽  
Li ◽  
Ding
Keyword(s):  
Nearest Neighbor ◽  
Detection Threshold ◽  
Radar Signal ◽  
False Alarms ◽  
Detection Accuracy ◽  
Ratio Test ◽  
Tracking Accuracy ◽  
Nearest Neighbor Algorithm ◽  
Multiple Hypothesis ◽  
Detection Processing

In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy.

IDC Infrasound technology path to continuous improvement

2020 ◽  
Author(s):  
Pierrick Mialle
Keyword(s):  
Automatic System ◽  
Detection Threshold ◽  
False Alarms ◽  
Detection Accuracy ◽  
Channel Correlation ◽  
Infrasound Signal ◽  
Lower Ratio ◽  
New Generation ◽  
Signal Characterization ◽  
Data Centres

<p>The IDC advances its methods and continuously improves its automatic system for the infrasound technology. The IDC focuses on enhancing the automatic system for the identification of valid signals and the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria. Alongside these efforts, the IDC and its partners also focuses on expanding the capabilities in NDC-in-a-Box (NiaB), which is a software package specifically aimed at the CTBTO user community, the National Data Centres (NDC).</p><p>An objective of this study is to illustrate the latest efforts by IDC to increase trust in its products, while continuing its infrasound specific effort on reducing the number of associated infrasound arrivals that are rejected from the automatic bulletins when generating the reviewed event bulletins. A number of ongoing projects at the IDC will be presented, such as: - improving the detection accuracy at the station processing stage by introducing the infrasound signal detection and interactive review software DTK-(G)PMCC (Progressive Multi-Channel Correlation) and by evaluating the performances of detection software; - development of the new generation of automatic waveform network processing software NET-VISA to pursue a lower ratio of false alarms over GA (Global Association) and a path for revisiting the historical IRED. The IDC identified a number of areas for improvement of its infrasound system, those will be shortly introduced.</p>

Flame Detection Using Generic Color Model and Improved Block-Based PCA in Active Infrared Camera

2018 ◽  
Vol 32 (05) ◽  
pp. 1850014
Author(s):  
Qian Zhao ◽  
Fengdong Sun ◽  
Wenhui Li ◽  
Peixun Liu
Keyword(s):  
Color Image ◽  
Color Space ◽  
Infrared Image ◽  
Infrared Camera ◽  
Detection Algorithm ◽  
Dynamic Features ◽  
Ycbcr Color Space ◽  
Imaging Results ◽  
Flame Detection ◽  
Block Based

In this paper, we proposed an all-weather flame detection algorithm which could make full use of active infrared cameras presently installed in many public places for surveillance purposes. Firstly, according to the different spectral imaging results in day and night, we propose a video type classification algorithm (VTCA) via imaging clues. VTCA could help us select different flame visual features in color image and infrared image. Secondly, we use a generic YCbCr-color-space-based chrominance model to extract regions of interest (ROI) of flame. Thirdly, two flame dynamic features are used to verify the candidate ROIs, which are common flame flicker feature and an improved block-based PCA in consecutive frames. The experimental results show that the proposed flame detection model has been successfully applied to various situations, including day and night, indoor and outdoor on our test video datasets, and it gives a better performance compared with other state-of-the-art methods.

scholarly journals DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1017-1024
Author(s):  
C. Theoharatos ◽  
A. Makedonas ◽  
N. Fragoulis ◽  
V. Tsagaris ◽  
S. Costicoglou
Keyword(s):  
Data Fusion ◽  
Target Detection ◽  
Single Channel ◽  
False Alarms ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Additional Advantage ◽  
Detection Model ◽  
Background Clutter ◽  
Fusion Scheme

Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

Sensorless Early Detection of Mechanical Faults: Developments in Smart Rotating Machines

2001 ◽  
Author(s):  
Alexander G. Parlos ◽  
Kyusung Kim ◽  
Raj M. Bharadwaj
Keyword(s):  
Fault Detection ◽  
Failure Modes ◽  
Fault Detection And Diagnosis ◽  
False Alarms ◽  
Detection Accuracy ◽  
Driving Dynamic ◽  
Diagnosis System ◽  
Electric Power Supply ◽  
Detection And Diagnosis ◽  
High Level

Abstract Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy and while exhibiting acceptably low false alarm rates. Such designs must have applicability to a large class of machines, require installation of no additional sensors, and require minimal detailed information regarding the specific machine design. Electromechanical systems, such as electric motors driving dynamic loads like pumps and compressors, often develop incipient failures that result in downtime. There is a large number of such failure modes, with a large majority being of mechanical nature. The precise signatures of these failure modes depend on numerous machine-specific factors, including variations in the electric power supply and driven load. In this paper the development and experimental demonstration of a sensorless, detection and diagnosis system is presented for incipient machine faults. The developed fault detection and diagnosis system uses recent developments in dynamic recurrent neural networks in implementing an empirical model-based approach, and multi-resolution signal processing for extracting fault information from transient signals. The signals used by the system are only the multi-phase motor current and voltage sensors, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting stator, rotor and bearing failures at early stages of development and during different levels of deterioration. Experimental test results from small machines, 2.2 kW, and large machines, 373 kW and 597 kW, are presented demonstrating the effectiveness of the proposed approach. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated.

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