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Sensing and Imaging for Defect Detection: 2nd Edition
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Sensing and Imaging for Defect Detection: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 458

Special Issue Editors

Prof. Dr. Haitao Wang

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: NDT&E technology with ultrasonic, electromagnetic; imaging processing technology; high-imaging-resolution technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yongkai Zhu

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: sensor technology; structural health monitoring technology
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Fei

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: sensor technology; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the progress made in science and technology, the quality of people's material and cultural lives has improved, meaning that the requirements for product quality and nondestructive testing technology have further increased. Nondestructive testing technology usually includes five conventional testing technologies, namely eddy current, penetration, magnetic particle, ultrasonic, and X-ray, along with their related new technologies. Usually, different materials need to be detected, and the defects that need to be detected are not the same—for example, for metals, defects include non-metallic pipe defects and slag inclusions, the metal not being welded through, porosity, etc.; for the power transmission of porcelain bottles such as ceramic materials, defects include cracks, porosity, etc.

This Special Issue calls for papers aimed at the detection of the most common defects, including surface defects, subsurface defects, and so on. Recent advances in sensor technologies form the basis of the development of nondestructive testing technology, data acquirement processing, and image processing technology.

The editors welcome the submission of high-quality research papers not previously published in other journals as well as review articles discussing recent advancements in the development of sensing and imaging techniques for defect detection technology that can be easily used in the NDT&E field.

Prof. Dr. Haitao Wang
Dr. Yongkai Zhu
Dr. Fei Fei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • sensors
  • NDT&E technology
  • defect detection technology
  • imaging technology
  • data acquirement and processing
  • sensing techniques

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Related Special Issue

Published Papers (1 paper)

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Research

20 pages, 31175 KiB  
Article
An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information
by Huixiang Liu, Xin Zhao, Qiong Liu and Wenbai Chen
Sensors 2024, 24(22), 7373; https://doi.org/10.3390/s24227373 - 19 Nov 2024
Viewed by 335
Abstract
Printed Circuit Boards (PCBs) are essential components in electronic devices, making defect detection crucial. PCB surface defects are diverse, complex, low in feature resolution, and often resemble the background, leading to detection challenges. This paper proposes the YOLOv8_DSM algorithm for PCB surface defect [...] Read more.
Printed Circuit Boards (PCBs) are essential components in electronic devices, making defect detection crucial. PCB surface defects are diverse, complex, low in feature resolution, and often resemble the background, leading to detection challenges. This paper proposes the YOLOv8_DSM algorithm for PCB surface defect detection, optimized based on the three major characteristics of defect targets and feature map visualization. First, to address the complexity and variety of defect shapes, we introduce CSPLayer_2DCNv3, which incorporates deformable convolution into the backbone network. This enhances adaptive defect feature extraction, effectively capturing diverse defect characteristics. Second, to handle low feature resolution and background resemblance, we design a Shallow-layer Low-semantic Feature Fusion Module (SLFFM). By visualizing the last four downsampling convolution layers of the YOLOv8 backbone, we incorporate feature information from the second downsampling layer into SLFFM. We apply feature map separation-based SPDConv for downsampling, providing PAN-FPN with rich, fine-grained shallow-layer features. Additionally, SLFFM employs the bi-level routing attention (BRA) mechanism as a feature aggregation module, mitigating defect-background similarity issues. Lastly, MPDIoU is used as the bounding box loss regression function, improving training efficiency by enhancing convergence speed and accuracy. Experimental results show that YOLOv8_DSM achieves a mAP (0.5:0.9) of 63.4%, representing a 5.14% improvement over the original model. The model’s Frames Per Second (FPS) reaches 144.6. To meet practical engineering requirements, the designed PCB defect detection model is deployed in a PCB quality inspection system on a PC platform. Full article
(This article belongs to the Special Issue Sensing and Imaging for Defect Detection: 2nd Edition)
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