Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanical Characteristics of Steel

Mechanical Characteristics of Steel

The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used...

You might also read

Related Articles

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

Sort by

Same author

Fault Diagnosis for Reducers Based on a Digital Twin.

Sensors (Basel, Switzerland)·2024

Same author

Primary central nervous system extranodal NK/T-cell lymphoma, nasal type with CD20 expression: Case report and review of the literature.

Neuropathology : official journal of the Japanese Society of Neuropathology·2023

Same author

A dual organelle-targeting photosensitizer based on curcumin for enhanced photodynamic therapy.

Journal of materials chemistry. B·2023

Same author

pH-Responsive NIR-II phototheranostic agents for in situ tumor vascular monitoring and combined anti-vascular/photothermal therapy.

Biomaterials·2023

Same author

Time series single-cell transcriptional atlases reveal cell fate differentiation driven by light in Arabidopsis seedlings.

Nature plants·2023

Same author

Polarity-Sensitive Probe for Two-Photon Fluorescence Lifetime Imaging of Lipid Droplets <i>In Vitro</i> and <i>In Vivo</i>.

Analytical chemistry·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

An Efficient Lightweight Method for Steel Surface Defect Detection.

Aiyun Zheng¹, Xinyu Jiang¹, Weimin Liu¹

¹College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces LCED-YOLO, an improved YOLOv11 model for detecting surface defects in steel production. The new model enhances edge detection and reduces parameters, achieving higher accuracy with less computational cost.

Keywords:

LDConv defect detection lightweight loss function

More Related Videos

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Related Experiment Videos

Last Updated: Jan 7, 2026

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Surface defects are common in steel production, posing challenges for traditional detection methods.
Accurate and efficient defect detection is crucial for quality control in the steel industry.

Purpose of the Study:

To develop an advanced deep learning model for precise steel surface defect detection.
To improve upon existing YOLOv11 capabilities for detecting complex defects with greater efficiency.

Main Methods:

Proposed LCED-YOLO model based on YOLOv11 architecture.
Incorporated C3K2-MSE module for enhanced edge information extraction.
Introduced LDConv and a lightweight decoupling head for model optimization.
Utilized a learnable attention factor with CIoU loss for improved localization of difficult samples.

Main Results:

LCED-YOLO achieved improved mAP50 scores of 79.8% on NEU-DET and 70.3% on GC10-DET datasets, outperforming YOLOv11.
Reduced model parameters by 19% and floating-point operations by 23%.
Demonstrated enhanced detection capability, particularly for challenging defect samples.

Conclusions:

LCED-YOLO offers a lightweight yet highly accurate solution for steel surface defect detection.
The proposed model effectively addresses the limitations of traditional methods and meets industrial demands for precision and efficiency.