Detection of Sliding Wheels and Hot Bearings Using Wayside Thermal Cameras

2016 ◽  
Author(s):  
Hanieh Deilamsalehy ◽  
Timothy C. Havens ◽  
Pasi Lautala
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Automatic Detection ◽  
Real Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Excessive Heat ◽  
Research Areas

Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

scholarly journals Distributed and collaborative Web Change Detection system

2015 ◽  
Vol 12 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Víctor Prieto ◽  
Manuel Álvarez ◽  
Víctor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Change Detection ◽  
Search Engines ◽  
Web Site ◽  
Detection System ◽  
Computational Cost ◽  
Web Pages ◽  
Web Page ◽  
Case Scenario ◽  
Worst Case ◽  
The Web

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

scholarly journals Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

2020 ◽  
Vol 10 (11) ◽  
pp. 3980 ◽  
Author(s):  
Cung Lian Sang ◽  
Bastian Steinhagen ◽  
Jonas Dominik Homburg ◽  
Michael Adams ◽  
Marc Hesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Measurement Data ◽  
Ultra Wideband ◽  
Machine Learning Techniques ◽  
Line Of Sight ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Worst Case Scenario

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

scholarly journals Automatic Detection and Localization of Macular Edema

2020 ◽  
Vol 5 (9) ◽  
pp. 552-558
Author(s):  
S. Sumithra ◽  
K. R. Remya ◽  
Dr. M. N. Giri Prasad
Keyword(s):  
Macular Edema ◽  
Vision Loss ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
The People ◽  
Simple Linear Iterative Clustering ◽  
Detection And Localization

Diabetic retinopathy is an eye disease and causes vision loss to the people who are suffering longer from the diabetes. Exudates, bright and red lesions are identified in the diabetic retinal eye. Automatic detection and localization of macular edema is a challenging issue since exudates have non uniform illumination and are low contrasted. Proposed algorithm to detect macular edema encompasses Simple Linear Iterative Clustering, Fisher linear discriminant and Support vector machine classifer. Optic Disc extraction prior to exudates extraction is also introduced. Performance of the proposed detection algorithm is tested on easily available databases: Diaretdb1, Messidor and E_optha Ex. Proposed method shows an accuracy of 97.81%, specificity 98.65 and Sensitivity 82.71%.

A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

2022 ◽  
Vol 22 (3) ◽  
pp. 1-17
Author(s):  
Chaonan Shen ◽  
Kai Zhang ◽  
Jinshan Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Social Learning ◽  
Early Detection ◽  
Detection System ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Internet Technology ◽  
Support Vector ◽  
Swarm Optimization

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

scholarly journals Intelligent Drive Assistance System

2021 ◽  
pp. 96-104
Author(s):  
Anuja Kale ◽  
Aditya Raut ◽  
Swati Sinha
Keyword(s):  
Computer Vision ◽  
Motor Vehicle ◽  
Detection System ◽  
Detection Algorithm ◽  
Economic Losses ◽  
Computer Engineering ◽  
Drowsiness Detection ◽  
Work Related ◽  
Physical Injuries ◽  
Major Trend

The report proposes the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver fatigue and sleepiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project. Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system. Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road. A person driving needs to be able to focus on driving at all instances. Any prolonged or sudden complications to the person driving the vehicle can cause serious accidents/damages. To ignore the importance of this could result in severe physical injuries, deaths and economic losses. Road incidents remain the leading type of fatal work-related event, carrying tremendous personal, social, and economic costs. While employers with a fixed worksite can observe and interact directly with workers in an effort to promote safety and reduce risk, employers with workers who operate a motor vehicle as part of their job have fewer options. Drowsiness detection system is regarded as an effective tool to reduce the number of road accidents. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. Haar face detection algorithm is used to capture frames of image as input and then the detected face as output.

scholarly journals Improved pseudolikelihood regularization and decimation methods on non-linearly interacting systems with continuous variables

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Alessia Marruzzo ◽  
Payal Tyagi ◽  
Fabrizio Antenucci ◽  
Andrea Pagnani ◽  
Luca Leuzzi
Keyword(s):  
State Of The Art ◽  
Real Data ◽  
Continuous Variables ◽  
Case Scenario ◽  
Testing Technique ◽  
Worst Case ◽  
Best Value ◽  
Interacting Systems ◽  
Effective Models ◽  
Rule Out

We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference based on pseudolikelihood maximization with \ell_1ℓ1 regularization and with decimation. In particular, we present a method to determine the best value of the regularizer parameter starting from a hypothesis testing technique. Concerning the decimation, we also analyze the worst case scenario in which there is no sharp peak in the tilded-pseudolikelihood function, firstly defined as a criterion to stop the decimation. Techniques are applied to noisy systems with non-linear dynamics, mapped onto multi-variable interacting Hamiltonian effective models for waves and phasors. Results are analyzed varying the number of available samples and the externally tunable temperature-like parameter mimicing real data noise. Eventually the behavior of inference procedures described are tested against a wrong hypothesis: non-linearly generated data are analyzed with a pairwise interacting hypothesis. Our analysis shows that, looking at the behavior of the inverse graphical problem as data size increases, the methods exposed allow to rule out a wrong hypothesis.

A novel single-lead handheld atrial fibrillation detection system

2021 ◽  
Author(s):  
Ying Li ◽  
Jianqing Li ◽  
Chenxi Yang ◽  
Yantao Xing ◽  
Chengyu Liu
Keyword(s):  
Atrial Fibrillation ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Early Screening ◽  
Rr Intervals ◽  
Daily Monitoring ◽  
Standard Device ◽  
Memory Cost ◽  
Sensitivity Specificity

Abstract Objective: The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved. This study proposed a novel AF detection system with a user-friendly interaction and a lightweight and accurate AF detection algorithm. Approach: The system consisted of a single-lead handheld electrocardiogram (ECG) device with a novel appearance like a gaming handle and a smartphone terminal embedded with AF detection. After feature optimization, the rule-based multi-feature AF detection algorithm had relatively good AF detection ability. Three types of experiments were designed to test the performance of the system. 1) Test the accuracy and time/memory cost of the AF detection algorithm. 2) Compare the proposed device with the standard device Shimmer. 3) Use the simulator to test the effectiveness of the system. Main results: The percentage of differences of successive RR intervals larger than 50 ms (PNN50), minimum value of RR intervals (minRR), and coefficient of sample entropy (COSEn) were features chosen for AF detection. 1) The sensitivity, specificity, and accuracy were 96.00%, 99.75%, 97.88% on the MIT-BIH AF database, and 98.50%, 94.50%, 96.50% on the clinical database we founded. The time/memory cost of the proposed algorithm was much smaller than that of Support Vector Machine (SVM). 2) The mean correlation coefficient of RR was 0.9950, indicating a high degree of consistency. 3) This system showed the effectiveness of AF detection. Significance: The proposed single-lead handheld AF detection system is demonstrated to be accurate, lightweight, consistent with the standard device, and efficient for AF detection.

scholarly journals Fruit Quality Detection and Gradation

2021 ◽  
pp. 383-388
Author(s):  
Harshal S. Deshmukh ◽  
Dr. S. W. Mohod ◽  
Dr. N. N. Khalsa
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Support Vector ◽  
Color Classification ◽  
Image Processing Techniques ◽  
Background Removal ◽  
Quality Detection ◽  
Analysis Methods ◽  
Processing Techniques

Grading and classification of fruits is based on observations and through experiences. The system exerts image- processing techniques for classification and grading the quality of fruits. Two-dimensional fruit images are classified on shape and color-based analysis methods. However, different fruit images have different or same color and shape values. Hence, using color or shape analysis methods are still not that much effective enough to identify and distinguish fruits images. Therefore, computer vision and image processing techniques have been found increasingly useful in the food industry, especially for applications in quality detection. Research in this area indicates the feasibility of using computer vision systems to improve product quality, the use of computer vision for the inspection of food has increased during recent years. This proposed work presents food quality detection system. The system design considers some feature that includes fruit colors and size, which increases accuracy for detection of roots pixels. Histogram of oriented gradients is used for background removal, for color classification, support vector machine is used.

scholarly journals Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 707 ◽  
Author(s):  
Yongchao Song ◽  
Yongfeng Ju ◽  
Kai Du ◽  
Weiyu Liu ◽  
Jiacheng Song
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Geometric Mean ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Light Illumination ◽  
Road Detection ◽  
The Road

Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.

Sign in / Sign up

Export Citation Format

Share Document