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Coatings
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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: 31 August 2026 | Viewed by 3038

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 250 words) can be sent to the Editorial Office for assessment.

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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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

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Research

22 pages, 2403 KB  
Article
A Method for Suppressing the Reflection of Coating Images on Aero-Engine Blades
by Xin Wen, Chengyan Han, Xiaoguang Liu, Kechen Song, Han Yu and Xingjie Li
Coatings 2025, 15(12), 1385; https://doi.org/10.3390/coatings15121385 - 26 Nov 2025
Viewed by 269
Abstract
Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading [...] Read more.
Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading to overexposure, underexposure, edge blurring and reduced measurement accuracy. To solve this, we propose ELANet, a deep-learning-based multi-exposure image fusion method with DenseNet as the backbone. Its key innovations include two parts: first, an Efficient Channel Attention mechanism to capture reflection feature differences between substrate and coating, prioritizing resource allocation to anti-reflection channels; second, an Ultra-Lightweight Subspace Attention Mechanism with only one-fifth the parameters of traditional spatial attention that adaptively assigns weights to local features based on curved surface reflection laws, enhancing edge and detail extraction while reducing computational cost. The Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism synergistically address exposure and blurring issues. Validated against 12 mainstream methods via 9 quantitative metrics, ELANet achieves state-of-the-art performance: MEF-SSIM reaches 0.9472, which is 1.3% higher than the best comparative method, PSNR reaches 21.48 dB, which is 2.2 percent higher than the second-best method, and the average processing time is 0.48 s. Ablation experiments confirm the necessity of the Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism. This method effectively supports high-precision blade inspection. Full article
(This article belongs to the Special Issue Solid Surfaces, Defects and Detection, 2nd Edition)
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26 pages, 5082 KB  
Article
Weed Detection on Architectural Heritage Surfaces in Penang City via YOLOv11
by Shaokang Chen, Yanfeng Hu, Yile Chen, Junming Chen and Si Cheng
Coatings 2025, 15(11), 1322; https://doi.org/10.3390/coatings15111322 - 12 Nov 2025
Viewed by 381
Abstract
George Town, the capital of Penang, Malaysia, was inscribed as a UNESCO World Heritage Site in 2008 and is renowned for its multicultural architectural surfaces. However, these historic façades face significant deterioration challenges, particularly biodeterioration caused by weed growth on wall surfaces under [...] Read more.
George Town, the capital of Penang, Malaysia, was inscribed as a UNESCO World Heritage Site in 2008 and is renowned for its multicultural architectural surfaces. However, these historic façades face significant deterioration challenges, particularly biodeterioration caused by weed growth on wall surfaces under hot and humid equatorial conditions. Root penetration is a critical surface defect, accelerating mortar decay and threatening structural integrity. To address this issue, this study proposes YOLOv11-SWDS (Surface Weed Detection System), a lightweight and interpretable deep learning framework tailored for surface defect detection in the form of weed intrusion on heritage buildings. The backbone network was redesigned to enhance the extraction of fine-grained features from visually cluttered surfaces, while attention modules improved discrimination between weed patterns and complex textures such as shadows, stains, and decorative reliefs. For practical deployment, the model was optimized through quantization and knowledge distillation, significantly reducing computational cost while preserving detection accuracy. Experimental results show that YOLOv11-SWDS achieved an F1 score of 86.0% and a mAP@50 of 89.7%, surpassing baseline models while maintaining inference latency below 200 ms on edge devices. These findings demonstrate the potential of deep learning-based non-destructive detection for monitoring surface defects in heritage conservation, offering both a reliable tool for sustaining George Town’s cultural assets and a transferable solution for other UNESCO heritage sites. Full article
(This article belongs to the Special Issue Solid Surfaces, Defects and Detection, 2nd Edition)
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27 pages, 51271 KB  
Article
Surface Damage Detection and Analysis for Reduction-Fired Cyan Square Bricks in Jiangnan Gardens via YOLOv12
by Lina Yan, Yile Chen, Xingkang Jia and Liang Zheng
Coatings 2025, 15(9), 1066; https://doi.org/10.3390/coatings15091066 - 11 Sep 2025
Viewed by 881
Abstract
As an outstanding UNESCO World Heritage Site, the Jiangnan gardens feature both exquisite and fragile components. Reduction-fired cyan square bricks, serving as crucial paving materials, are long-term exposed to natural and anthropogenic factors, making them prone to various types of surface damage and [...] Read more.
As an outstanding UNESCO World Heritage Site, the Jiangnan gardens feature both exquisite and fragile components. Reduction-fired cyan square bricks, serving as crucial paving materials, are long-term exposed to natural and anthropogenic factors, making them prone to various types of surface damage and urgently requiring efficient, non-destructive detection methods to support scientific conservation. Traditional manual inspection methods suffer from low efficiency, strong subjectivity, and potential disturbance to the fragile heritage structures. This study focuses on developing an intelligent detection method based on advanced computer vision, employing the YOLOv12 object detection model to achieve non-contact, automated identification of typical tile surface damage types in the Jiangnan gardens (such as cracking, stains, water stains, and wear). A total of 691 images of reduction-fired cyan square bricks collected on-site were used as training samples. The main conclusions of this study are as follows: (1) By constructing a dataset containing multiple samples and multiple scenes of reduction-fired cyan square brick images in Jiangnan gardens, the YOLOv12 model was trained and optimized, enabling it to accurately identify subtle damage features under complex texture backgrounds. (2) Overall indicators: Through the comparison of the confusion matrices of the four key training nodes, model C (the 159th epoch, highest mAP50–95) has the most balanced overall performance in multiple categories, with an accuracy of 0.73 for cracking, 0.77 for wear, 0.60 for water stain, and 0.65 for stains, which can meet basic detection requirements. (3) Difficulty of discrimination: Compared with stains and water stains, cracking and wear are easier to distinguish. Experimental results indicate that the detection method is feasible and effective in identifying the surface damage types of reduction-fired cyan square bricks in Jiangnan gardens. This research provides a practical and efficient “surface technology” solution for the preventive protection of cultural heritage, contributing to the sustainable preservation and management of world heritage. Full article
(This article belongs to the Special Issue Solid Surfaces, Defects and Detection, 2nd Edition)
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Graphical abstract

19 pages, 3527 KB  
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
Cited by 2 | Viewed by 1025
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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