scholarly journals Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

2021 ◽  
Vol 11 (18) ◽  
pp. 8388
Author(s):  
Bubryur Kim ◽  
Ronnie O. Serfa Juan ◽  
Dong-Eun Lee ◽  
Zengshun Chen
Keyword(s):  
Visual Recognition ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Thermal Image ◽  
Solar Panel ◽  
Cumulative Distribution ◽  
Apparent Temperature ◽  
Photovoltaic Module ◽  
Thermal Images ◽  
Detection Analysis

Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.

scholarly journals Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7396
Author(s):  
Bubryur Kim ◽  
Se-Woon Choi ◽  
Gang Hu ◽  
Dong-Eun Lee ◽  
Ronnie O. Serfa Serfa Juan
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Multivariate Analysis ◽  
Visual Recognition ◽  
Thermal Image ◽  
Cumulative Distribution ◽  
Graph Representations ◽  
Detection Analysis ◽  
Processing Techniques

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

scholarly journals Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 988
Author(s):  
Kshirasagar Naik ◽  
Tejas Pandit ◽  
Nitin Naik ◽  
Parth Shah
Keyword(s):  
Internet Of Things ◽  
Activity Recognition ◽  
Thermal Model ◽  
Thermal Image ◽  
Heat Radiation ◽  
Household Activity ◽  
Thermal Images ◽  
Gas Leakage ◽  
3D Space ◽  
Image Pairs

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.

scholarly journals Spectral Filtering Copper Coated Hibiscus-Methanol Extract for Photovoltaic Module Applications

2019 ◽  
Vol 9 (2) ◽  
pp. 2894-2897
Keyword(s):  
Methanol Extract ◽  
Weather Conditions ◽  
Solar Panel ◽  
Photovoltaic Module ◽  
Spectral Filtering ◽  
Pv Panel ◽  
Harmful Radiation

This research focuses on ways to protect the photovoltaic solar panel from harmful radiation during harsh weather conditions. The bio-filter made up of copper coated hibiscus extract from methanol was proposed. It was discovered that the bio-filter was able stabilize the fluctuations and in some cases improved on the output of the PV panel. It was recommended that the onward study on this kind of bio-filter would enhance higher patronage of PV products in the African market.

Comparison of Infrared Image Pre-Processing Technique on Electronic Power Equipment

2013 ◽  
Vol 680 ◽  
pp. 339-344
Author(s):  
Hong Men ◽  
Xin Su ◽  
Peng Chen ◽  
Jia Xue Yu
Keyword(s):  
Fault Diagnosis ◽  
Target Identification ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Power Equipment ◽  
Low Snr ◽  
Fuzzy Image ◽  
High Pass ◽  
Subsequent Target

The disadvantages of infrared image are low resolution, bad stereoscopic sense, fuzzy image and low SNR, according to the application of infrared image in fault diagnosis of electronic power equipment, in this paper ,we make a comparative research on pre-processing technique of image de-noising and enhancement, and propose an infrared image enhancement algorithm based on platform histogram equalization combined with enhanced high-pass filtering, the algorithm can effectively improve the contrast by comparison, it is obvious to the noise effect, highlighting the objectives and details, and makes a good foundation for the subsequent target identification and fault diagnosis.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1328-1340
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1175-1187
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2017 ◽  
Vol 6 (2) ◽  
pp. 18-32
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

First Order Statistical Features Thermal Images for Surge Arrester Fault Classification

2016 ◽  
Vol 818 ◽  
pp. 91-95
Author(s):  
Novizon ◽  
Zulkurnain Abdul-Malek
Keyword(s):  
Neural Network ◽  
Extraction Method ◽  
Thermal Image ◽  
Image Features ◽  
Fault Classification ◽  
Contact Method ◽  
Health States ◽  
Thermal Images ◽  
First Order ◽  
Condition Diagnosis

— Thermal imaging technique is a very convenient, versatile and non-contact method which has been used for fault condition diagnosis of electrical equipment. The fault condition diagnosis is composed with data acquisition, data pre-processing, data analysis and decision making. Some important features contain in thermal image can be extracted for equipment condition monitoring and fault diagnosis. This paper attempts to extract some important features from the zinc oxide (ZnO) surge arrester using first order statistical histogram extraction to classify the fault condition using neural network. The experimental work was carried out to capture thermal image of 120 kV rated ZnO surge arrester. The thermal images were segmented and plotted histogram using dedicated software. Some features such as the maximum, minimum, mean, standard deviation, and variance were extracted using the extraction method, classification of aging was carried out using the neural network based on the leakage current values. The health states consist of normal, defection and faulty. The results show that the thermal image features extracted using the extraction method can be used to classify the fault condition of the ZnO surge arresters

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