· Over the past few decades, there has been an increase in the number of aging civil structures worldwide, most of which are made of concrete. Concrete may lose strength as a result of continuous loading and environmental factors. It has been challenging to detect corrosion damage in industrial and civil constructions, and the existing
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· A schematic diagram for machine parts defect detection in model assembly line. BER rates of the shape descriptor on machine part images of MPEG-7 CE Part-B dataset. Figures-uploaded by
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· Cracks can develop in RCC slabs due to various factors, including: 1. Shrinkage: Concrete undergoes shrinkage as it cures, which can result in small surface cracks known as shrinkage cracks. These are typically hairline cracks and are considered normal. Proper curing practices can help minimize them.
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· Researchers use machine learning to detect defects in additive manufacturing. Longitudinal (top) and axial (middle) images of X-Ray CT data of parts with 6 internal defects: a spherical clog, a stellated shaped clog, a cone shaped void, a blob shaped void, an elliptical warp of the inner channel, and a nonconcentric center nozzle.
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:1024 × 780Stochastic gradient descent (SGD) · In surface defect detection, FCNNs are trained on a dataset of images containing both defective and non-defective surfaces, allowing the network to learn
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Innovative defect-detection techniques, particularly machine vision and deep-learning methods [54–56], have become the most popular in recent years and are one of the key technologies for automating defect detection due to their versatility and lack of reliance on human assistance.
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A real time system fusing data from vision and laser sensors to detect and classify types of defects in brick masonry is presented. A Support Vector Machine (SVM) algorithm is conceived and used to develop a Defect Finding Classification Model (DFCM) to automatically classify the types of defects found in masonry walls using the image data
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:Deep LearningAutomated Defect InspectionCivil Engineering · The current research introduces the knowledge base for applying deep learning to classify and detect RC bridges' five most common defects (cracks, corrosion,
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· A growing body of research also focuses on the detection of masonry defects in the 2D/3D survey data increasingly using machine learning or deep learning (Idjaton et al. 2022;Kwon and Yu 2019
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:Defect Detection ManufacturingDeep Learning For Defect InspectionSNF-YOLOv8 can improve the accuracy of detecting small-sized defects and distinguishing between normal and defective areas on the surface of stamped parts while
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· Applying a supervised machine learning algorithm, the K-Nearest Neighbor (KNN), to our quantified numeric data revealed that LC3B provided a strong measure for discriminating clear cell RCC (ccRCC).
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· It is supposed that the electrode defect sites with high local electric field generate numerous current paths through dielectric ceramic on the cross section of MLCCs [27, 28], which causes a significant increase in leakage current [29, 30].
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· The inability to detect cracks and other more significant defects can be fixed by using a more robust steel defect detection method like Defect Detection on Hot-Rolled Flat Steel Products proposed
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· The above merits make the developed RCC ˜ ∗ indicator a desired tool to detect bearing incipient defects and assess the bearing’s health condition. 4.3. Detecting the incipient bearing defect with the low-variance version of the form factor index4.3.1.
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Use ResNet50 deep learning model to predict defects on steel sheets and visually localize the defect using Res-UNET model. This project aims to predict surface defects on steel sheets from images. This computer vision technique leverages transfer learning using pretrained ResNet50 model.
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Different Types of Failures in RCC Buildings with Case Studies. Types of failures in reinforced concrete buildings such as failure due to poor concrete quality, reinforcement corrosion, failure of foundations due to soil etc. with case studies are discussed. In small residential buildings the quality of construction is seldom questionable.
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· In this paper, we present an automatic system designed for detect the presence of split defects in sheet-metal forming processes. The image acquisition system includes basically a CCD progressive
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AI-powered defect analysis revolutionizes manufacturing by efficiently identifying defects such as scratches, dents, cracks, and color variations with impressive accuracy. The benefits of AI-driven defect analysis include improved accuracy, increased efficiency, reduced costs, and enhanced product quality. The use of advanced algorithms and
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Reinforced concrete is of vital importance in many civil and industrial structural applications. The effective bonding between steel and concrete is the core guarantee of the safe operation of the structures. Corrosion or
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The geometrical quality assessment for additive manufacturing (AM) is a great challenge because of the complexity of AM parts and low repeatability of AM processes. Existing defect detection algorithms with 3D data mainly use features comprised of point-to-point distance difference between the design and manufactured objects. This study introduced
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Once deployed, Vanti automatically monitors and visualizes model performance, alerts to model performance issues (such as slipping accuracy), and auto-discovers new model opportunities. To learn more about Vanti’s AI-powered solution: Read the larger technical overview. Get the specifics of our defect detection solution.
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· With the rise of Industrial IoT (IIoT) and Industry 4.0, ML is playing an ever-increasing role in enabling automation. This includes the use of computer vision to analyze and detect defects with products like raw materials on assembly lines, as well as out in the field (e.g., to detect metal fatigue). With this in mind, we built an ML model
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Packaging is one of the most important aspects in the food industry. The problems faced during packaging can classified into two categories, defects in the packaging before the substrate is filled and after the substrate is filled. There are methods to determine the defects in the food containers after the food has been packed by means of measuring
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:Deep LearningMachine LearningArtificial Neural NetworksVisual inspection is a crucial component of AI-driven defect analysis in manufacturing. It enables the detection of complex defects, including cosmetic flaws and intricate surfaces. The naked eye might miss that. Deep learning-based visual inspection systems offer higher accuracy and efficiency than traditional manual quality control methods.
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· The machine vision-based defect-detection methods are suitable for the detection of surface defects in products, which has achieved up to 88.60% accuracy in binary defect-detection problems []. The defect-detection accuracy over scratches, holes, scales, pitting, edge cracks, crusting, and inclusions can reach 95.30% [ 109 ].
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· Abstract. The Software Defect Prediction (SDP) model engages in predicting defects and bugs in the software. This model will detect and predict bugs during early stages of the software development life cycle to improve the overall quality of software and reduce the cost also. In this paper, the author presents a model that will predict the
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· This paper presents a deep learning approach to identify and classify various defects in the laser-directed energy manufactured components. It mainly focuses on the Convolutional Neural Network (CNN) architectures, such as VGG16, AlexNet, GoogLeNet and ResNet to perform the automated classification of defects. The main
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In this paper, we present an automatic system designed for detect the presence of split defects in sheet-metal forming processes. The image acquisition system includes basically a CCD progressive camera and a diffuse illumination system mounted on the endeffector of a 6-dof robot. The inspection-robot displaces the image acquisition system over the
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· Deep learning algorithms are implemented to efficiently detect defects from the collected images, and a simultaneous localization and mapping algorithm is adopted for site reconstruction with the acquired LiDAR data.
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Implementation of automatic computer-aided identification system to recognize different types of welding defects in radiographic images which includes defect detection and classification using Deep Neural
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Trituradora de piedra vendida por proveedores certificados, como trituradoras de mandíbula/cono/impacto/móvil, etc.
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