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Showing 1 to 5 of 5 for “"Crack Segmentation"”.

  1. LLM-as-Judge for Reliable Crack Segmentation in Edge-Based Structural Inspection

    <p>Crack detection and segmentation are fundamental tasks in structural health monitoring, enabling early identification of damage in critical infrastructure such as roads, bridges, and buildings. While deep learning models—particularly convolutional encoder--decoder architectures—have achieved …

    usm Repository record for LLM-as-Judge for Reliable Crack Segmentation in Edge-Based Structural Inspection (opens in a new tab)

  2. Crack identification through computer vision: from non-learning-based to learning-based methodologies, and from patch-level to pixel-level detections

    … one of the most common types of defects is cracking, which evolves rapidly under the impacts of heavy traffic, aging of materials, and drastic environmental changes. In recent decades, image-based automated crack detection methodologies have been developed and extensively applied by …

    alabama Repository record for Crack identification through computer vision: from non-learning-based to learning-based methodologies, and from patch-level to pixel-level detections (opens in a new tab)

  3. Multi-Class 3D Segmentation of Progressive Damage in Advanced Composites using Deep Learning

    … three categories, each of them manifesting as a crack of some sort: (i) delamination (interlaminar cracking), (ii) matrix cracking, and (iii) fiber breakage (crack through fiber). At present, X-ray computed tomography (CT) performed to observe these complex damage mechanisms in 3D and 4D (3D …

    mit Repository record for Multi-Class 3D Segmentation of Progressive Damage in Advanced Composites using Deep Learning (opens in a new tab)

  4. Computer Vision Applications in Structural Engineering

    … on tall industrial chimneys. 4. RTK-UAV based 3D Crack Identification, Visualisation and Quantification for Structural Health Monitoring: This study proposed enhancements for 3D Crack Identification, Visualisation, and Quantification in Structural Health Monitoring using a Real-Time-Kinematic …

    exeter

  5. 3D damage mapping and segmentation using neural radiance fields and advanced deep learning techniques

    … Time Agumentation (TTA) significantly enhances crack detection capabilities. This approach allows for precise mapping of segmented cracks onto a 3D model of a bridge, offering a detailed and quantifiable assessment of structural damage. By combining these innovative technologies, the research …

    manitoba Repository record for 3D damage mapping and segmentation using neural radiance fields and advanced deep learning techniques (opens in a new tab)