scholarly journals Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6219
Author(s):  
Jhon Jairo Vega Díaz ◽  
Michiel Vlaminck ◽  
Dionysios Lefkaditis ◽  
Sergio Alejandro Orjuela Vargas ◽  
Hiep Luong
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Support Vector ◽  
Solar Panels ◽  
Low Contrast ◽  
Whole Process ◽  
Imaging Sensors ◽  
Processing Techniques

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

scholarly journals Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

2020 ◽  
Vol 9 (3) ◽  
pp. 1137-1148
Author(s):  
Jafar Majidpour ◽  
Hiwa Hasanzadeh
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Computer Networks ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Training Time ◽  
Modern Computer ◽  
Normal Behavior ◽  
Review Protocol ◽  
Future Work

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.

scholarly journals The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Dileep Kumar Soother ◽  
Jawaid Daudpoto ◽  
Nicholas R. Harris ◽  
Majid Hussain ◽  
Sanaullah Mehran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Diagnostic Techniques ◽  
Diagnostic Methods ◽  
Future Directions ◽  
Diagnosis And Prognosis ◽  
Feature Processing ◽  
Tremendous Progress ◽  
Motor Condition ◽  
Processing Techniques

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

scholarly journals Auto-Threshold Deep SVDD for Anomaly-based Web Application Firewall

2021 ◽  
Author(s):  
Ali Moradi Vartouni ◽  
Matin Shokri ◽  
Mohammad Teshnehlab
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Web Application ◽  
Threshold Level ◽  
Web Security ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Deep Support

Protecting websites and applications from cyber-threats is vital for any organization. A Web application firewall (WAF) prevents attacks to damaging applications. This provides a web security by filtering and monitoring traffic network to protect against attacks. A WAF solution based on the anomaly detection can identify zero-day attacks. Deep learning is the state-of-the-art method that is widely used to detect attacks in the anomaly-based WAF area. Although deep learning has demonstrated excellent results on anomaly detection tasks in web requests, there is trade-off between false-positive and missed-attack rates which is a key problem in WAF systems. On the other hand, anomaly detection methods suffer adjusting threshold-level to distinguish attack and normal traffic. In this paper, first we proposed a model based on Deep Support Vector Data Description (Deep SVDD), then we compare two feature extraction strategies, one-hot and bigram, on the raw requests. Second to overcome threshold challenges, we introduce a novel end-to-end algorithm Auto-Threshold Deep SVDD (ATDSVDD) to determine an appropriate threshold during the learning process. As a result we compare our model with other deep models on CSIC-2010 and ECML/PKDD-2007 datasets. Results show ATDSVDD on bigram feature data have better performance in terms of accuracy and generalization. <br>

Comparison between mathematical morphology and Canny Edge Detection methods for image post processing techniques in segmenting microcalcification

2018 ◽  
Author(s):  
Aminah Abdul Malek ◽  
Ummu Mardhiah Abdul Jalil ◽  
Dayangku Nur Faizah Pg Mohamad ◽  
Nurul Ain Muhamad ◽  
Sharifah Syafiyah Syed Hashim
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Detection Methods ◽  
Post Processing ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Processing Techniques

Decoding of graphically encoded numerical digits using deep learning and edge detection techniques

2021 ◽  
pp. 1-8
Author(s):  
P. V. S. M. S. Kartik ◽  
Konjeti B. V. N. S. Sumanth ◽  
V. N. V. Sri Ram ◽  
G. Jeyakumar
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Graphical Representation ◽  
Training Data ◽  
Text And Image ◽  
Detection Techniques ◽  
Probability Prediction ◽  
Classification Of Images ◽  
Processing Techniques

The encoding of a message is the creation of the message. The decoding of a message is how people can comprehend, and decipher the message. It is a procedure of understanding and interpretation of coded data into a comprehensible form. In this paper, a self-created explicitly defined function for encoding numerical digits into graphical representation is proposed. The proposed system integrates deep learning methods to get the probabilities of digit occurrence and Edge detection techniques for decoding the graphically encoded numerical digits to numerical digits as text. The proposed system’s major objective is to take in an Image with digits encoded in graphical format and give the decoded stream of digits corresponding to the graph. This system also employs relevant pre-processing techniques to convert RGB to text and image to Canny image. Techniques such as Multi-Label Classification of images and Segmentation are used for getting the probability of occurrence. The dataset is created, on our own, that consists of 1000 images. The dataset has the training data and testing data in the proportion of 9 : 1. The proposed system was trained on 900 images and the testing was performed on 100 images which were ordered in 10 classes. The model has created a precision of 89% for probability prediction.

scholarly journals Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alejandro Lopez-Rincon ◽  
Alberto Tonda ◽  
Lucero Mendoza-Maldonado ◽  
Daphne G. J. C. Mulders ◽  
Richard Molenkamp ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Methods ◽  
Added Value ◽  
Detection Methods ◽  
Minimal Amount ◽  
Specific Detection ◽  
Data Repositories ◽  
Primer Sets ◽  
Global Initiative ◽  
Virus Strains

AbstractIn this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Research of edge detection methods in the task of pattern recognition on images with low contrast

2021 ◽  
Vol 1 ◽  
pp. 123-128
Author(s):  
E.V. Belyaeva ◽  
Keyword(s):  
Pattern Recognition ◽  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Detection Methods ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Low Contrast ◽  
Gaussian Filters

The article discusses edge detection methods separately and combinations of edge detection filters with antialiasing filters in the task of pattern recognition on images with low contrast. Sobel, Canny, Otsu and thresholding filters are considered as edge detection methods. Median and Gaussian filters are considered as smoothing filters. The performance of the filters is assessed using the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM).

scholarly journals Auto-Threshold Deep SVDD for Anomaly-based Web Application Firewall

2021 ◽  
Author(s):  
Ali Moradi Vartouni ◽  
Matin Shokri ◽  
Mohammad Teshnehlab
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Web Application ◽  
Threshold Level ◽  
Web Security ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Deep Support

Protecting websites and applications from cyber-threats is vital for any organization. A Web application firewall (WAF) prevents attacks to damaging applications. This provides a web security by filtering and monitoring traffic network to protect against attacks. A WAF solution based on the anomaly detection can identify zero-day attacks. Deep learning is the state-of-the-art method that is widely used to detect attacks in the anomaly-based WAF area. Although deep learning has demonstrated excellent results on anomaly detection tasks in web requests, there is trade-off between false-positive and missed-attack rates which is a key problem in WAF systems. On the other hand, anomaly detection methods suffer adjusting threshold-level to distinguish attack and normal traffic. In this paper, first we proposed a model based on Deep Support Vector Data Description (Deep SVDD), then we compare two feature extraction strategies, one-hot and bigram, on the raw requests. Second to overcome threshold challenges, we introduce a novel end-to-end algorithm Auto-Threshold Deep SVDD (ATDSVDD) to determine an appropriate threshold during the learning process. As a result we compare our model with other deep models on CSIC-2010 and ECML/PKDD-2007 datasets. Results show ATDSVDD on bigram feature data have better performance in terms of accuracy and generalization. <br>

scholarly journals Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

2020 ◽  
Vol 9 (4) ◽  
pp. 670
Author(s):  
Aliyu Muhammad Abdu ◽  
Musa Mohd Muhammad Mokji ◽  
Usman Ullah Ullah Sheikh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Precision Agriculture ◽  
Plant Disease ◽  
Image Data ◽  
Training Data ◽  
Support Vector ◽  
Disease Detection ◽  
Processing Techniques

Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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