scholarly journals Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Husein Perez ◽  
Joseph H.M. Tah ◽  
Amir Mosavi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Effective Means ◽  
Scale Up ◽  
Health And Safety ◽  
Condition Assessment ◽  
Automated Detection ◽  
Current Asset ◽  
Maintenance Work ◽  
Assessment Procedures

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

scholarly journals Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3556 ◽  
Author(s):  
Husein Perez ◽  
Joseph H. M. Tah ◽  
Amir Mosavi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Effective Means ◽  
Scale Up ◽  
Real Life ◽  
Health And Safety ◽  
Condition Assessment ◽  
Automated Detection ◽  
Proposed Model ◽  
Current Asset

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

Data processing using deep learning of the generative-adversarial neural network (GAN)

2021 ◽  
Author(s):  
V.Y. Ilichev ◽  
I.V. Chukhraev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Effective Means ◽  
Network Models ◽  
Neural Network Models ◽  
High Quality ◽  
Python Language ◽  
And Training

The article is devoted to the consideration of one of the areas of application of modern and promising computer technology – machine learning. This direction is based on the creation of models consisting of neural networks and their deep learning. At present, there is a need to generate new, not yet existing, images of objects of different types. Most often, text files or images act as such objects. To achieve a high quality of results, a generation method based on the adversarial work of two neural networks (generator and discriminator) was once worked out. This class of neural network models is distinguished by the complexity of topography, since it is necessary to correctly organize the structure of neural layers in order to achieve maximum accuracy and minimal error. The described program is created using the Python language and special libraries that extend the set of commands for performing additional functions: working with neural networks Keras (main library), integrating with the operating system Os, outputting graphs Matplotlib, working with data arrays Numpy and others. A description is given of the type and features of each neural layer, as well as the use of library connection functions, input of initial data, compilation and training of the obtained model. Next, the implementation of the procedure for outputting the results of evaluating the errors of the generator and discriminator and the accuracy achieved by the model depending on the number of cycles (eras) of its training is considered. Based on the results of the work, conclusions were drawn and recommendations were made for the use and development of the considered methodology for creating and training generative and adversarial neural networks. Studies have demonstrated the procedure for operating with comparatively simple and accessible, but effective means of a universal Python language with the Keras library to create and teach a complex neural network model. In fact, it has been proved that the use of this method allows to achieve high-quality results of machine learning, previously achievable only when using special software systems for working with neural networks.

scholarly journals A Deep Learning Approach to Detect Pornography Videos in Educational Repositories

2020 ◽  
Author(s):  
Pedro V. A. de Freitas ◽  
Antonio J. G. Busson ◽  
Álan L. V. Guedes ◽  
Sérgio Colcher
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Automated Detection ◽  
Learning Approach ◽  
Multimodal Approach ◽  
Deep Convolutional Neural Networks ◽  
High Level

A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.

scholarly journals Assessment of Convolution Neural Networks for Wetland Mapping with Landsat in the Central Canadian Boreal Forest Region

2019 ◽  
Vol 11 (7) ◽  
pp. 772 ◽  
Author(s):  
Darren Pouliot ◽  
Rasim Latifovic ◽  
Jon Pasher ◽  
Jason Duffe
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Anthropogenic Activities ◽  
Effective Means ◽  
Wetland Classification ◽  
Convolution Neural Networks ◽  
Spatial Extension ◽  
Accuracy Measure ◽  
Recent Climate

Methods for effective wetland monitoring are needed to understand how ecosystem services may be altered from past and present anthropogenic activities and recent climate change. The large extent of wetlands in many regions suggests remote sensing as an effective means for monitoring. Remote sensing approaches have shown good performance in local extent studies, but larger regional efforts have generally produced low accuracies for detailed classes. In this research we evaluate the potential of deep-learning Convolution Neural Networks (CNNs) for wetland classification using Landsat data to bog, fen, marsh, swamp, and water classes defined by the Canada Wetland Classification System (CWCS). The study area is the northern part of the forested region of Alberta where we had access to two reference data sources. We evaluated ResNet CNNs and developed a Multi-Size/Scale ResNet Ensemble (MSRE) approach that exhibited the best performance. For assessment, a spatial extension strategy was employed that separated regions for training and testing. Results were consistent between the two reference sources. The best overall accuracy for the CWCS classes was 62–68%. Compared to a pixel-based random forest implementation this was 5–7% higher depending on the accuracy measure considered. For a parameter-optimized spatial-based implementation this was 2–4% higher. For a reduced set of classes to water, wetland, and upland, overall accuracy was in the range of 86–87%. Assessment for sampling over the entire region instead of spatial extension improved the mean class accuracies (F1-score) by 9% for the CWCS classes and for the reduced three-class level by 6%. The overall accuracies were 69% and 90% for the CWCS and reduced classes respectively with region sampling. Results in this study show that detailed classification of wetland types with Landsat remains challenging, particularly for small wetlands. In addition, further investigation of deep-learning methods are needed to identify CNN configurations and sampling methods better suited to moderate spatial resolution imagery across a range of environments.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Urban Railway Bridge Fast Evaluation Based on Cloud Computing

2011 ◽  
Vol 71-78 ◽  
pp. 4501-4505
Author(s):  
Ming Chen ◽  
Wan Zhou
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Railway Bridge ◽  
Fast Evaluation ◽  
Railway Bridges ◽  
Computing Model ◽  
Bridge Condition Assessment

Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge

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