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Concrete crack detection, image classification: Type of data: 2D-RGB image (.jpg) How data was acquired: Original images of cracked and non-cracked concrete bridge decks, walls, and pavements were captured using a 16 MP Nikon digital camera. Data format: Raw digital images (.jpg) Experimental factors: Experimental …
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Traditional machine learning and image processing: Smoothing, white lane line detection, image normalization, saturation ... The publicly available "Concrete …
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In this paper, a dataset was prepared using a multipurpose pavement inspection vehicle to detect cracks and acquire images. The database contains 8000 …
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To build reliable image classifiers you need enough diverse datasets with accurately labeled data. Image classification with CNN works by sliding a kernel or a filter across the input image to capture relevant details in the form of features. The most important image classification metrics include Precision, Recall, and F1 Score. 💡 Read …
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from Concrete Surface Images Using Machine Learning Hyunjun Kim, Eunjong Ahn, Myoungsu Shin and Sung-Han Sim Abstract In concrete structures, surface cracks are important indicators of structural ...
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Popular Image Classification Datasets 1. MNIST. The MNIST database of handwritten digits is one of the most classic machine learning datasets. With 60,000 training images and 10,000 test images of 0-9 digits (10 classes of digits), MNIST is excellent for benchmarking image classification models.
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Then, we apply transfer learning for image classification to enhance the classification of wellbore cement isolation. In transfer learning stage, the images are …
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Standardizing the data. Our image are already in a standard size (180x180), as they are being yielded as contiguous float32 batches by our dataset. However, their RGB channel values are in the [0, 255] range. This is not ideal for a neural network; in general you should seek to make your input values small.
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To create the dataset, images in .jpg format were used, in the resolution in which they were taken (i.e. 3464 × 4618 px to 3840 × 5120 px), without affecting the quality of the image.
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Crack is the earliest indication of structural deterioration. It is necessary to examine the structural elements as cracks can affect the durability and safety of civil infrastructures. Visual inspection of cracks is laborious and time-consuming, rendering the technique unsuitable for inspection of numerous structural elements. Therefore, it is …
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In an image classification task, the network assigns a label (or class) to each input image. However, suppose you want to know the shape of that object, which pixel belongs to which object, etc. In this case, you need to assign a class to each pixel of the image—this task is known as segmentation. A segmentation model returns much more ...
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By leveraging FHE, Concrete ML can unlock a myriad of new use cases for machine learning, such as enabling secure and private data collaboration, protecting sensitive data while still allowing for analysis, and facilitating machine learning on data-sets that are subject to strict data privacy regulations, for instance
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Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression, and even outlier detection tasks. SVMs can be used for a variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection, and …
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Han and Golparvar-Fard developed a construction material library (CML) based on C-SVM classifiers with linear x 2 kernels on 100 × 100, 75 × 75, and 50 × 50 pixel-images datasets of cement-based surfaces, paving, brick, asphalt, formwork, foliage, concrete, marble, gravel, insulation, metal, soil, wood, stone, and waterproofing paint. …
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A methodology for identifying concrete cracks using machine learning is presented, particularly designed for classifying cracks and noncrack noise patterns that are otherwise difficult to distinguish using existing image processing algorithms. In concrete structures, surface cracks are important indicators of structural durability and …
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1. Introduction. Machine learning plays an important role in computational intelligence. Many learning classifiers (e.g., support vector machine, fuzzy k-nearest neighboring, and neural networks with fuzzy systems) and intelligent algorithms have been used for computer-aided systems including adaptive force control of machining …
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3.1 Dataset. In this paper, we tested approach on the challenging multi-objective multi-class image dataset of bridges with large overlapping concrete defects, known as CODEBRIM [].This publicly available dataset contains 1,590 high-resolution images from 30 bridges, resulting in a total of 2,506 background image blocks and …
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Summary. Ensuring the integrity of wellbore cement is critical for the environmental protection, safety, and economic viability of oil, gas, geothermal, and Carbon Capture …
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In structural health monitoring and condition assessment, deterioration detection and forecast are fundamental aspects. 1 Civil infrastructures, including buildings, bridges, power-station, dams, and so on are essential parts of society, and most of these structures are made of concrete. However, these concrete structures deteriorate …
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Introducing Convolutional Neural Networks. A breakthrough in building models for image classification came with the discovery that a convolutional neural network (CNN) could be used to progressively extract higher- and higher-level representations of the image content. Instead of preprocessing the data to derive …
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This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the …
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A critical challenge is to automatically identify cracks from an image containing actual cracks and crack-like noise patterns (e.g. dark shadows, stains, lumps, …
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The combination of aggregate, cement, and water is a composite construction material which is popularly known as concrete. It is a commonly used manufactured item worldwide for construction purpose which has varied properties (Lomborg 2001).Depending on the need of the work the strength and appearance of concrete will be attained by the …
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Then, we apply transfer learning for image classification to enhance the classification of wellbore cement isolation. In transfer learning stage, the images are processed using several pre-trained image classifiers—Xception, VGG16, MobileNetV2, and ResNet50—originally trained on the extensive ImageNet database containing over …
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from Concrete Surface Images Using Machine Learning Hyunjun Kim, Eunjong Ahn, Myoungsu Shin and Sung-Han Sim ... for the natural image classification of objects such as 2 Structural Health ...
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Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning. April 2018; Structural Health Monitoring 18(1):6874; ... image classification proces s, ...
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Image Classification Using Machine Learning; CNN Image Classification (Deep Learning) Example Applications of Image Classification; Let's dive deep into it! About us: Viso.ai provides the end-to-end Computer Vision Infrastructure, Viso Suite. It's a powerful all-in-one solution for AI vision. Companies worldwide use it to develop and ...
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For the purpose of image classification, it was decided to change the classification of such an image to "Uncracked". ... Shin, M. & Sim, S. H. Crack and …
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Current concrete surface crack detection methods cannot simultaneously achieve high detection accuracy and efficiency. Thus, this study focuses on the recognition and classification of crack images and proposes a concrete crack detection method that integrates the Inception module and a quantum convolutional neural network. First, the …
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This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network …
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