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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal crystal...
Lumber Defects01:23

Lumber Defects

Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
X-ray Crystallography02:18

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...

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Related Experiment Video

Updated: Jun 13, 2026

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

MGDR-YOLO: An Efficient Multi-Backbone YOLOv11 Framework for X-Ray Weld Defect Inspection.

Jiuyang Yu1, Pan Liu1, Yaonan Dai2

  • 1Hubei Provincial Engineering Technology Research Center of Green Chemical Equipment, School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

MGDR-YOLO enhances X-ray weld defect detection by improving accuracy and speed. This novel approach significantly boosts the detection of challenging defects, making it ideal for industrial inspection.

Keywords:
X-ray weld defect detectiondirectional convolutiongated attention fusionmulti-backbone networksre-parameterized shared detection head

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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

Related Experiment Videos

Last Updated: Jun 13, 2026

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Materials Science

Background:

  • X-ray weld seam imaging presents challenges like weak contrast, slender structures, and multi-scale features.
  • Existing detectors struggle with accurate and efficient detection of subtle weld defects.

Purpose of the Study:

  • To develop an industrially deployable detector, MGDR-YOLO, for improved X-ray weld defect detection.
  • To enhance the accuracy, robustness, and real-time performance of weld defect identification.

Main Methods:

  • Proposed MGDR-YOLO with four key innovations: MultiBackbone, Gated Attention Fusion Block (GAFB), Directional Feature Convolution (DFConv), and Rep Shared Convolutional Detection Head (RSCD).
  • MultiBackbone enables complementary direction-detail and context modeling.
  • GAFB facilitates selective feature fusion, DFConv optimizes directional feature extraction, and RSCD enhances detection head efficiency.

Main Results:

  • MGDR-YOLO achieved a mean average precision (mAP) of 95.2%, outperforming YOLOv11n (92.9%).
  • Significant mAP improvement of 10.1 percentage points for low-contrast (LP) defects.
  • Increased frames per second (FPS) by 39.4% while reducing parameters by 46.2%.

Conclusions:

  • MGDR-YOLO offers superior accuracy and robustness in X-ray weld defect detection.
  • The model maintains real-time performance, suitable for resource-constrained industrial applications.
  • Demonstrates effectiveness in detecting challenging, slender, and low-contrast weld defects.