Book ChapterDOI
Non-destructive testing for cracks in concrete
Deepali Koppad,Nirmala Paramanandham +1 more
- Vol. 656, pp 657-664
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TLDR
A convolution neural network model has been developed and trained using both positive (crack images) and negative (non-crack) images and initial results are obtained using a neural network for concrete crack detection.Abstract:
Non-destructive testing (NDT) is the process of analyzing the materials, components, structures, etc. without causing damage to it. In this paper, a NDT technique is proposed for detecting the cracks in the concrete surfaces. Initial results are obtained using a neural network for concrete crack detection. A convolution neural network model has been developed and trained using both positive (crack images) and negative (non-crack) images. In this work, a database consisting of 40,000 images is used. The model is trained with 36,000 images, 4000 for validation and 4000 for testing. To evaluate the effectiveness of model, accuracy, recall, precision and F1 score parameters are calculated.read more
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Journal ArticleDOI
Vision Based Crack Detection in Concrete Structures Using Cutting-Edge Deep Learning Techniques
TL;DR:
Journal ArticleDOI
The Auxiliary Engine Lubricating Oil Pressure Monitoring System Based on Modbus Communication
TL;DR: In this article , a reliable and efficient alarm monitoring system for marine vessels is presented, which can generate alarms with a time delay less then one second since the abnormal condition appears. But the system is not suitable for real-time monitoring and efficient processing time in abnormal machine conditions.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
Journal ArticleDOI
Crack detection using image processing: A critical review and analysis
Arun Mohan,Sumathi Poobal +1 more
TL;DR: In this paper, a detailed survey is conducted to identify the research challenges and the achievements till in this field, and those research papers are reviewed based on the image processing techniques, objectives, accuracy level, error level, and the image data sets.