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Advancements in Sensing Technologies for Structural Health Monitoring and Digital Twinning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1872

Special Issue Editor


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Guest Editor
Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
Interests: structural health monitoring; digital image correlation; modal identification

Special Issue Information

Dear Colleagues,

In structural health monitoring applications, sensing technology plays a crucial role, particularly in digital twinning for highly complex structures. In this special issue, we aim to explore the latest developments in sensing technologies, such as high-speed, high-resolution, non-contact, and wireless techniques (e.g. RFID quantifiable sensors), which can be applied to sense the online responses of the structure of interest. Subjects of studies relevant to sensor performance, sensor data communication, sensor placement, and selection are welcome. Data assimilation from sensor models of the structure to make informed decisions is also encouraged. The integration of sensor fusion and reduced-order modelling for the purpose of online digital twinning and decision-making is also a key focus.

Dr. Weizhuo Wang
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • dgital twinning
  • sensing technologies
  • wireless techniques
  • sensor fusion

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Published Papers (1 paper)

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Research

22 pages, 4908 KiB  
Article
A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection
by Liefa Liao, Chao Song, Shouluan Wu and Jianglong Fu
Sensors 2025, 25(3), 769; https://doi.org/10.3390/s25030769 - 27 Jan 2025
Viewed by 1490
Abstract
To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters used for defect detection, [...] Read more.
To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters used for defect detection, particularly the interplay between feature extraction, fusion, and bounding box regression, which often leads to inefficiencies in traditional methods. YOLOv10n-SFDC incorporates advanced elements such as the DualConv module, SlimFusionCSP module, and Shape-IoU loss function, improving feature extraction, fusion, and bounding box regression to enhance accuracy. Testing on the NEU-DET dataset shows that YOLOv10n-SFDC achieves a mean average precision (mAP) of 85.5% at an Intersection over Union (IoU) threshold of 0.5, a 6.3 percentage point improvement over the baseline YOLOv10. The system uses only 2.67 million parameters, demonstrating efficiency. It excels in identifying complex defects like ’rolled in scale’ and ’inclusion’. Compared to SSD and Fast R-CNN, YOLOv10n-SFDC outperforms these models in accuracy while maintaining a lightweight architecture. This system excels in automated inspection for industrial environments, offering rapid, precise defect detection. YOLOv10n-SFDC emerges as a reliable solution for the continuous monitoring and quality assurance of steel surfaces, improving the reliability and efficiency of steel manufacturing processes. Full article
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