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Coatings
Special Issues
Solid Surfaces, Defects and Detection, 2nd Edition
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Solid Surfaces, Defects and Detection, 2nd Edition

A special issue of Coatings (ISSN 2079-6412).

Deadline for manuscript submissions: 25 October 2025 | Viewed by 109

Special Issue Editor

Dr. Kechen Song

E-Mail Website
Guest Editor
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Interests: applied surface science; vision detection for surface defects; multi-modal image analysis and application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite submissions to this Special Issue on the subject of solid surfaces, defects, and detection. This is a continuation of a successful previous Special Issue (https://www.mdpi.com/journal/coatings/special_issues/surface_defect).

As an important application of coatings, the solid surface has been widely used in various industrial coating fields. Due to problems of material properties and process flow, the solid surface will inevitably produce many defects, such as cracks and shrinkage holes. These defects seriously affect the quality of products; therefore, timely detection is needed.

Accordingly, we have launched this new Special Issue of Coatings, which will collect original research articles and review papers focusing on the fundamentals and application of applied surface science and engineering for coatings. We invite papers dealing with, but not limited to, the following topics:

  • Coatings for solid surfaces;
  • Theoretical and computational modeling of solid surfaces;
  • Vision detection for surface defects;
  • Artificial intelligence in vision detection;
  • Recognition of industrial products;
  • Hidden defect detection and classification methods;
  • Non-destructive testing and evaluation using image processing methods.

We look forward to receiving your contributions.

Dr. Kechen Song
Guest Editor

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. Coatings is an international peer-reviewed open access monthly 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

  • coatings for solid surfaces
  • vision detection for surface defects
  • defect classification
  • artificial intelligence of vision detection
  • non-destructive testing

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Related Special Issue

Published Papers (1 paper)

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Research

19 pages, 3527 KiB  
Article
BBW YOLO: Intelligent Detection Algorithms for Aluminium Profile Material Surface Defects
by Zijuan Yin, Haichao Li, Bo Qi and Guangyue Shan
Coatings 2025, 15(6), 684; https://doi.org/10.3390/coatings15060684 - 6 Jun 2025
Abstract
This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced [...] Read more.
This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced YOLOv8 model is proposed for aluminum profile material surface-defect detection. First, the model can effectively eliminate redundant feature information and enhance the feature-extraction process by incorporating a weighted Bidirectional Feature Pyramid Feature-fusion Network (BiFPN). Second, the model incorporates a dynamic sparse-attention mechanism (BiFormer) along with an efficient pyramidal network architecture, which enhances the precision and detection speed of the model. Meanwhile, the model optimizes the loss function using Wise-IoU v3 (WIoU v3), which effectively enhances the localization performance of surface-defect detection. The experimental results demonstrate that the precision and recall of the BBW YOLO model are improved by 5% and 2.65%, respectively, compared with the original YOLOv8 model. Notably, the BBW YOLO model achieved a real-time detection speed of 292.3 f/s. In addition, the model size of BBW YOLO is only 6.3 MB. At the same time, the floating-point operations of BBW YOLO are reduced to 8.3 G. As a result, the BBW YOLO model offers excellent defect detection performance and opens up new opportunities for its efficient development in the aluminum industry. Full article
(This article belongs to the Special Issue Solid Surfaces, Defects and Detection, 2nd Edition)
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