Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

233
Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
233
Shear on the Horizontal Face of a Beam Element01:16

Shear on the Horizontal Face of a Beam Element

282
To understand shear on the flat side of a prismatic beam element, consider the vertical and horizontal shearing forces, and the normal forces, acting on the element. The element's upper (U) and lower (L) sections, which are divided by the beam's neutral axis, are examined. The equilibrium of these forces is determined by applying the equilibrium equation, which helps identify the horizontal shearing force. This force is directly related to the bending moments and the cross-section's...
282
Steel Manufacturing01:26

Steel Manufacturing

788
Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
During this smelting process, limestone plays a crucial role by forming slag. Slag captures impurities within the molten iron, such...
788
Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

163
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
163
Steel Fastening Techniques01:17

Steel Fastening Techniques

225
Steel sections can be joined together through various fastening techniques including riveting, bolting, and welding, each suitable for different structural requirements and conditions.
Rivets are cylindrical steel fasteners with a specially designed head. During application, rivets are heated until white-hot and then inserted through pre-drilled holes in the steel sections. A pneumatic hammer is used to shape the exposed end into a second head, securing the sections together.
Bolting is another...
225
Design of Prismatic Beams for Bending01:23

Design of Prismatic Beams for Bending

367
The design of prismatic beams, structural elements with a uniform cross-section, focuses on ensuring safety and structural integrity under load. The design process begins by determining the allowable stress, either from material properties tables, or by dividing the material's ultimate strength by a safety factor. This safety factor is essential for accommodating uncertainties, and varies depending on the material—timber, steel, or concrete—with each having unique strength and...
367

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fluorobenzene-Mediated Dragging Effect Boosting Bulk/Interfacial Ion Transport Enables -50°C Operation of Long-Life Potassium-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Bioorthogonal release-mediated targeted degradation of tripartite motif containing 24 protein for atherosclerosis therapy.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Dipole-Spin Synergy in PdO/YMn<sub>2</sub>O<sub>5</sub> Enables Fast Ozone Decomposition from -45 to >45 °C at High Humidity.

Environmental science & technology·2026
Same author

pH-responsive nano immunomodulator for rheumatoid arthritis therapy via macrophages pyroptosis inhibiting and reprograming.

Bioactive materials·2026
Same author

Loss of PMR4 callose synthase triggers jasmonic acid-dependent resistance to the clubroot disease in Arabidopsis and Brassica napus.

The Plant cell·2026
Same author

Synthetic-Dimensions-Engineered Fiber-Optic Tamm Plasmon Metatips Enabling High-Dimensional Manipulation for Enhanced Hydrogen Sensing.

ACS nano·2026

Related Experiment Video

Updated: Sep 10, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K

SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation.

Rui Wen1, Wu Xie1, Yong Fan2

  • 1Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China.

Journal of Imaging
|August 27, 2025
PubMed
Summary

A new method called SABE-YOLO improves weld seam instance segmentation for automated welding. It enhances structural and boundary features, achieving higher accuracy with efficient computational performance.

Keywords:
YOLOdeep learninginstance segmentationweld seam recognition

More Related Videos

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel
07:18

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel

Published on: August 13, 2019

7.1K
Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing
08:40

Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing

Published on: February 11, 2016

11.7K

Related Experiment Videos

Last Updated: Sep 10, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K
Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel
07:18

Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel

Published on: August 13, 2019

7.1K
Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing
08:40

Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing

Published on: February 11, 2016

11.7K

Area of Science:

  • Computer Vision
  • Robotics
  • Materials Science

Background:

  • Automated welding systems require precise weld seam recognition for path planning and quality control.
  • Current industrial vision approaches for weld seam instance segmentation face challenges with complex geometries and blurred edges.
  • Existing models struggle with accurate boundary perception and structural representation of elongated weld seams.

Purpose of the Study:

  • To develop an advanced weld seam instance segmentation method addressing limitations in boundary perception and structural representation.
  • To enhance the accuracy and efficiency of visual perception for complex weld seam structures in automated welding.
  • To introduce a novel approach that improves localization accuracy and feature extraction for weld seams.

Main Methods:

  • Proposed a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) model for weld seam instance segmentation.
  • Introduced a Structure-Aware Fusion Module (SAFM) utilizing strip pooling attention and element-wise multiplicative fusion for enhanced structural features.
  • Developed a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) for improved edge sensitivity and multi-scale feature aggregation.
  • Implemented an Inner-MPDIoU loss function to boost localization accuracy.

Main Results:

  • SABE-YOLO achieved a 46.3% AP(50-95) metric, outperforming YOLOv8n-Seg by 3 percentage points.
  • The model demonstrated efficient computational performance with 18.3 GFLOPs and 6.6M parameters.
  • Achieved a high inference speed of 127 FPS, indicating a favorable balance between accuracy and efficiency.
  • Experimental results validated the effectiveness on a self-built weld seam image dataset.

Conclusions:

  • The proposed SABE-YOLO provides an effective solution for high-precision visual perception of complex weld seam structures.
  • The method shows strong potential for practical industrial applications in automated welding.
  • SABE-YOLO offers a superior trade-off between segmentation accuracy and computational efficiency compared to existing methods.