scholarly journals Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit

2018 ◽  
Vol 8 (10) ◽  
pp. 1772 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Yiwen Liu ◽  
Chao Tan
Keyword(s):  
Thermal Image ◽  
Support Vector ◽  
Temporal Context ◽  
Accurate Identification ◽  
Thermal Images ◽  
Tracking Method ◽  
Online Identification ◽  
Spatio Temporal ◽  
Infrared Thermal Images ◽  
Cutting Unit

In order to achieve accurate identification of a shearer cutting state, infrared thermal images were creatively adopted in this paper. As the position of a shearer cutting unit is constantly changing, and the temperature in the vicinity is obviously distinct, mathematical morphology theory was used to detect the cutting unit in an infrared thermal image. Furthermore, a target tracking method is put forward to achieve cutting unit tracking based on the combination of morphology and a spatio-temporal context (STC) algorithm. Then, the temperature field features of this tracking area were extracted, and an intelligent classifier based on a support vector machine (SVM) was constructed to identify the cutting state of the shearer. Some experiments are presented, and the results indicate the feasibility and superiority of the proposed method.

scholarly journals Facial Paralysis Detection in Infrared Thermal Images Using Asymmetry Analysis of Temperature and Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2309
Author(s):  
Xulong Liu ◽  
Yanli Wang ◽  
Jingmin Luan
Keyword(s):  
Temperature Distribution ◽  
Early Diagnosis ◽  
Facial Paralysis ◽  
Texture Feature ◽  
Texture Features ◽  
Healthy People ◽  
Support Vector ◽  
Thermal Images ◽  
Asymmetry Analysis ◽  
Infrared Thermal Images

Facial temperature distribution in healthy people shows contralateral symmetry, which is generally disrupted by facial paralysis. This study aims to develop a quantitative thermal asymmetry analysis method for early diagnosis of facial paralysis in infrared thermal images. First, to improve the reliability of thermal image analysis, the facial regions of interest (ROIs) were segmented using corner and edge detection. A new temperature feature was then defined using the maximum and minimum temperature, and it was combined with the texture feature to represent temperature distribution of facial ROIs. Finally, Minkowski distance was used to measure feature symmetry of bilateral ROIs. The feature symmetry vectors were input into support vector machine to evaluate the degree of facial thermal symmetry. The results showed that there were significant differences in thermal symmetry between patients with facial paralysis and healthy people. The accuracy of the proposed method for early diagnosis of facial paralysis was 0.933, and the area under the ROC curve was 0.947. In conclusion, temperature and texture features can effectively quantify thermal asymmetry caused by facial paralysis, and the application of machine learning in early detection of facial paralysis in thermal images is feasible.

scholarly journals Double Thresholding with Sine Entropy for Thermal Image Segmentation

2021 ◽  
Vol 38 (6) ◽  
pp. 1713-1718
Author(s):  
Manikanta Prahlad Manda ◽  
Daijoon Hyun
Keyword(s):  
Image Segmentation ◽  
Shannon Entropy ◽  
Thermal Image ◽  
Image Databases ◽  
Misclassification Error ◽  
Average Sensitivity ◽  
Thermal Images ◽  
Range Correlation ◽  
Infrared Thermal Images ◽  
State Of Art

Traditional thresholding methods are often used for image segmentation of real images. However, due to distinct characteristics of infrared thermal images, it is difficult to ensure an optimal image segmentation using the traditional thresholding algorithms, and therefore, sometimes this can lead to over-segmentation, missing object information, and/or spurious responses in the output. To overcome these issues, we propose a new thresholding technique that makes use of the sine entropy-based criterion. Moreover, we build a double thresholding technique that makes use of two thresholds to get the final image thresholding result. Besides, we introduce the sine entropy concept as a supplement of the Shannon entropy in creating threshold-dependent criterion derived from the grayscale histogram. We found that the sine entropy is more robust in interpreting the strength of the long-range correlation in the gray levels compared to the Shannon entropy. We have experimented with our method on several infrared thermal images collected from standard image databases to describe the performance. On comparing with the state-of-art methods, the qualitative results from the experiments show that the proposed method achieves the best performance with an average sensitivity of 0.98 and an average misclassification error of 0.01, and second-best performance with an average sensitivity of 0.99 and an average specificity of 0.93.

scholarly journals Thermal Image Based Fault Diagnosis of Gears using Support Vector Machines

2019 ◽  
Vol 9 (1) ◽  
pp. 155-160
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Thermal Image ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Thermal Images ◽  
Rotary Machinery ◽  
Vector Machines ◽  
Experimental Findings

Condition monitoring and fault diagnosis of working machines have gained significant attention due to their prospective benefits, such as enhanced productivity, decreased repair and maintenance costs and enhanced machine operation. In this paper, a thermal image based non-contact methodology has been proposed to diagnose the gear faults using support vector machines (SVM). The thermal images acquired from gearbox simulator were preprocessed using 2D-discrete wavelet transform to decompose the thermal images. The relevant features were extracted from converted thermal gray-scaled images followed by selecting the strongest feature using Mahalanobis distance criteria. Finally, the selected features were given to a SVM classifier for classifying the different gear faults. The experimental findings indicate that fault diagnosis using thermography for rotary machinery can be put into practice to industrial fields as a new smart fault diagnostic method with excellent prediction performance.

On-Site Identification of Zero Resistance Insulator Based on Infrared Thermal Image and Weights-Direct-Determination Neural Network

2013 ◽  
Vol 441 ◽  
pp. 417-420
Author(s):  
Tang Bing Li ◽  
Lei Gong ◽  
Jian Gang Yao ◽  
Yan Jun Kuang ◽  
Bin Bin Rao
Keyword(s):  
Neural Network ◽  
Transmission Lines ◽  
Direct Determination ◽  
Thermal Image ◽  
Absolute Deviation ◽  
Thermal Images ◽  
Zero Resistance ◽  
Infrared Thermal Images ◽  
Site Identification ◽  
The Relationship

A method using infrared thermal images and weights-direct-determination neural network (WDDNN) to identify the zero resistance insulators on-site is presented. The basic procedures were as follows: the infrared thermal image were denoised, intensified, segmented, and a rectangular which was regarded as object was intercepted in the insulators chain; in view of the relationship between gray value of infrared thermal images and temperature of object surface, four parameters which stand for standard deviation, absolute deviation, quartiles and range of gray value, were extracted directly; these four parameters were used as the input of WDDNN to train the model, which could be used identifing the zero resistance insulators after being trained. This method can effectively avoid the interference of transmission lines, and can meet the real-time require when identifying on-site. Experimental results verify the feasibility and effectiveness of this method.

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