Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing—Third Edition

Topic Information

Dear Colleagues,

In industrial production, particularly in sectors involving metal parts and components, complex processes such as machining, stamping, precision casting, powder metallurgy, injection molding, and other specialized synthesis procedures are integral. These processes require stringent control to ensure the desired product quality. Metal parts have widespread applications across various industries and are essential to daily life. However, given the variety of part types and sizes and the complexities involved in tasks such as surface inspection, size measurement, and target positioning, these processes often face challenges in terms of precision and accuracy. Traditional manual inspection techniques cannot meet the growing demands of modern production.

To address these challenges in industrial production, the integration of intelligent detection systems powered by artificial intelligence (AI) offers significant promise. These AI-based solutions can learn and recognize key information, such as surface defects, dimensions, and the precise positioning of metal parts. Unlike traditional vision algorithms, advanced and optimized AI algorithms can effectively mitigate issues arising from significant reflection, glare, or brightness during image acquisition, ensuring high recognition speeds, enhanced accuracy, and versatility. These AI-powered systems have the potential to revolutionize various production processes by offering robust solutions to longstanding challenges. The application of AI in smart industrial diagnostics and manufacturing (SIDM-AI) is an area of great potential, merging AI technologies with vision processing solutions. AI is a rapidly evolving field that encompasses a broad range of technologies, including robotics, language recognition, image recognition, and natural language processing. As AI has matured, its capabilities to simulate human intelligence have advanced significantly, enabling machines to perform tasks that once required human cognition. While AI may not replicate human consciousness, its ability to process information and make decisions allows it to perform tasks with precision and efficiency, often exceeding human capabilities.

In this context, AI’s role in industrial quality inspection processes, including surface inspection, assembly inspection, precision measurement, and workpiece positioning, is becoming increasingly important. Compared to traditional manual inspection, AI-based solutions offer a more cost-effective, faster, and more accurate alternative. This shift is particularly transformative in reducing reliance on labor-intensive manual inspection while improving product quality. The advancement of SIDM-AI is already enhancing industries like automobile manufacturing, building materials, 3C manufacturing, and textiles. Moreover, the integration of AI technologies with medical image processing, particularly in biomedical engineering, is contributing to the advancement of healthcare diagnostics. Medical image processing, combined with AI, is revolutionizing the detection, analysis, and interpretation of medical images, enabling faster and more accurate diagnoses. AI can significantly enhance the ability to identify and diagnose medical conditions from imaging modalities such as CT scans, MRIs, and X-rays, offering real-time, automated, and highly accurate analyses of medical data.

Given the potential of AI to enhance both the industrial and biomedical sectors, this Topic will explore the state of the art in AI-based smart diagnostics, with a focus on visual detection, computer vision technologies, and medical image processing. The aim is to showcase the latest advancements in these fields, emphasizing the synergy between AI and biomedical engineering in the context of medical image processing. We will also examine how these innovations can benefit industries by reducing production costs, improving operational efficiency, and accelerating the transition toward more intelligent, automated environments. Contributions to this collection will highlight cutting-edge research and applications that bridge AI, industrial diagnostics, and medical image processing, with a particular focus on how these technologies are transforming both the manufacturing and healthcare sectors. This body of work will underscore the growing role of AI in advancing technological solutions that provide both economic and societal benefits.

Suggested topics include, but are not limited to, the following:

• Smart image identification based on computer vision technology;
• AI-driven medical image segmentation and diagnosis in biomedical engineering;
• Intelligent detection based on machine learning;
• Visual classification based on machine learning;
• Smart detection of images based on AI;
• Segmentation tasks of images based on AI;
• The fusion of images based on AI;
• Smart industrial analysis based on machine learning;
• Smart industrial diagnostics based on machine learning.

Prof. Dr. Kelvin Wong
Prof. Dr. Andrew W. H. Ip
Prof. Dr. Dhanjoo N. Ghista
Prof. Dr. Wenjun (Chris) Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.1	2017	16.5 Days	CHF 1800	Submit
Machines machines	2.1	3.0	2013	15.5 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Technologies technologies	4.2	6.7	2013	21.1 Days	CHF 1600	Submit

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19 pages, 11999 KiB  

Open AccessArticle

PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection

by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng and Jianhua Yang

Appl. Sci. 2025, 15(8), 4343; https://doi.org/10.3390/app15084343 - 15 Apr 2025

Viewed by 209

Abstract

Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboard Defect-You Only Look Once), a lightweight YOLO-based algorithm with [...] Read more.

Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboard Defect-You Only Look Once), a lightweight YOLO-based algorithm with multi-scale feature fusion and weak texture enhancement capabilities. In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. This approach improved the mAP (mean average precision) of weakly featured but edge-sensitive defects (such as scratches) by as much as 20.9%. In order to improve the algorithm’s ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. ShareSepHead fused feature maps from different scales of the neck network by adding a convolutional layer with shared weights, and the C2f_SAC module adaptively fused multi-rate receptive fields through a switching mechanism. The synergistic effect of ShareSepHead and C2f_SAC improved the detection accuracy of multi-scale defects by 10.6–20.8%. The experimental results demonstrated that PBD-YOLO achieved 85.6% mAP at 50% intersection over union (IoU) and 81.4% recall, surpassing YOLOv10 by 5.5% and 13%, respectively, while reducing parameters by 11.3%. In summary, it could be better to meet the need of accurately detecting surface defects on particleboard. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing—Third Edition)

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