Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks

2020 ◽  
Vol 162 ◽  
pp. 249-256 ◽  
Author(s):  
Alejandro Rico Espinosa ◽  
Michael Bressan ◽  
Luis Felipe Giraldo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Solar Photovoltaic ◽  
Rgb Images ◽  
Photovoltaic Plants

scholarly journals Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2930 ◽  
Author(s):  
Søren Skovsen ◽  
Mads Dyrmann ◽  
Anders Mortensen ◽  
Kim Steen ◽  
Ole Green ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Botanical Composition ◽  
Data Simulation ◽  
Fully Convolutional Neural Networks ◽  
Rgb Images ◽  
Using Data

scholarly journals TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 139-146
Author(s):  
C. Yang ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Infrared Band ◽  
Rgb Images ◽  
Geospatial Databases

<p><strong>Abstract.</strong> Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%.</p>

scholarly journals Monkey Features Location Identification Using Convolutional Neural Networks

2018 ◽  
Author(s):  
Rollyn Labuguen (P) ◽  
Vishal Gaurav ◽  
Salvador Negrete Blanco ◽  
Jumpei Matsumoto ◽  
Kenichi Inoue ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Animal Behavior ◽  
Natural Environment ◽  
Feature Detection ◽  
Natural Habitat ◽  
Deep Convolutional Neural Network ◽  
Rgb Images

AbstractUnderstanding animal behavior in its natural habitat is a challenging task. One of the primary step for analyzing animal behavior is feature detection. In this study, we propose the use of deep convolutional neural network (CNN) to locate monkey features from raw RGB images of monkey in its natural environment. We train the model to identify features such as the nose and shoulders of the monkey at about 0.01 model loss.

scholarly journals Ice Detection on Rotor Blades of Wind Turbines using RGB Images and Convolutional Neural Networks

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 1292-1297 ◽  
Author(s):  
Markus Kreutz ◽  
Abderrahim Ait Alla ◽  
Anatoli Eisenstadt ◽  
Michael Freitag ◽  
Klaus-Dieter Thoben
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Wind Turbines ◽  
Rotor Blades ◽  
Rgb Images ◽  
Ice Detection

scholarly journals A New Radiomic Study on Lung CT Images of Patients with Covid-19 using LBP and Deep Learning (Convolutional Neural Networks (CNN))

2020 ◽  
Author(s):  
Hüseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Ct Images ◽  
Gray Level ◽  
Speed Up ◽  
Rgb Images ◽  
Short Time ◽  
Lung Ct

Abstract The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, the amount of 25 lung CT images in total (15 of Covid-19 patients and 10 of normal) was multiplied (250 images in total) using three data augmentation methods which relate to contrast change, brightness change and noise addition, and these images were subjected to automatic classification. Within the scope of the study, experiments were made for each case which include the use of the CT images of lungs (gray-level and RGB) directly, the images obtained by applying Local Binary Pattern (LBP) to these images (gray-level and RGB) and the images obtained by combining these images (gray-level and RGB). In the study, a 23-layer Convolutional Neural Networks (CNN) architecture was developed and used in classification processes. Leave-one-group-out cross validation method was used to test the proposed system. In this context, the result of the study indicated that the best AUC and EER values were obtained for the combination of original (RGB) and LBP applied (RGB) images, and these figures are 0,9811 and 0,0445 respectively. It was observed that, applying LBP to images, the use of CNN input causes an increase in sensitivity values while it causes a decrease in values of specificity. The highest sensitivity was obtained for the case of using LBP-applied (RGB) images and has a value of 0,9947. Within the scope of the study, the highest values of specificity and accuracy were obtained by the help of CT of lungs (gray-level) with 0,9120 and 95,32%, respectively. The results of the study indicate that analyzing images of lung CT using deep learning methods in diagnosing Covid-19 disease will speed up the diagnosis and significantly reduce the burden on healthcare workers.

scholarly journals Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariela Fernández-Campos ◽  
Yu-Ting Huang ◽  
Mohammad R. Jahanshahi ◽  
Tao Wang ◽  
Jian Jin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Accuracy ◽  
Rater Agreement ◽  
Deep Convolutional Neural Networks ◽  
Wheat Blast ◽  
High Classification Accuracy ◽  
Rgb Images ◽  
Model Training ◽  
Wheat Spike

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

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