2D/3D Industrial Visual Inspection and Intelligent Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 265

Special Issue Editors

School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
Interests: industrial 2D/3D visual inspection; intelligent image processing
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Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: optical 3D measurement; machine vision; online/onsite intelligent sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201418, China
Interests: online visual inspection; robot calibration

Special Issue Information

Dear Colleagues,

In recent years, online and onsite optical metrology techniques have received extensive attention for many dimensional control research and industrial applications. This Special Issue focuses on the advancements and applications of 2D and 3D industrial visual inspection and intelligent image processing technologies. With the increasing demand for automation, precision, and efficiency in industrial settings, visual inspection has become a cornerstone for quality control, defect detection, and dimensional measurement. This issue explores state-of-the-art methods for 2D and 3D measurement, visual inspection, and real-time analysis powered by artificial intelligence, machine learning, and computer vision. Topics include innovative approaches to defect detection, metrology, image processing, along with their integration into smart manufacturing systems.

Additionally, this Special Issue highlights challenges such as handling complex geometries, high-speed measurement, and measurement data processing, as well as solutions leveraging deep learning and hybrid algorithms. Contributions that address emerging trends, such as the fusion of 2D and 3D data, cloud-based inspection systems, and the “Smart +” measurement, are particularly encouraged. This Special Issue aims to provide a platform for researchers and practitioners to share insights and innovations that drive the evolution of industrial visual inspection toward greater intelligence and autonomy.

The technical scope of this Special Issue includes, but is not limited to, the following:

  • 2D and 3D optical metrology techniques;
  • Intelligent image processing;
  • Accuracy and efficiency improvements for optical metrology;
  • Vision detection or 3D reconstruction using UAV;
  • Robot-integrated optical metrology systems;
  • AI-assisted optical metrology;
  • Online/onsite optical metrology solutions;
  • 2D and 3D AOI;
  • Large-scale measurement;
  • Point cloud processing;
  • Measurement uncertainty;
  • 3D endoscopic metrology;
  • Event camera-based 3D metrology.

Dr. Xiao Yang
Dr. Xiaobo Chen
Dr. Chengyi Yu
Prof. Dr. Jinsong Bao
Guest Editors

Manuscript Submission Information

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Keywords

  • optical metrology
  • online/onsite inspection
  • image processing
  • deep learning
  • point cloud processing
  • uncertainty analysis
  • event camera
  • endoscopic metrology

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Published Papers (1 paper)

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Research

28 pages, 6379 KiB  
Article
Research on Lithium-Ion Battery Diaphragm Defect Detection Based on Transfer Learning-Integrated Modeling
by Lihua Ye, Xu Zhao, Zhou He, Zixing Zhang, Qinglong Zhao and Aiping Shi
Electronics 2025, 14(9), 1699; https://doi.org/10.3390/electronics14091699 - 22 Apr 2025
Viewed by 165
Abstract
Ensuring the security and reliability of lithium-ion batteries necessitates the development of a robust methodology for detecting defects in battery separators during production. This study initially uses data augmentation techniques in the data processing phase, followed by the utilization of the weighted random [...] Read more.
Ensuring the security and reliability of lithium-ion batteries necessitates the development of a robust methodology for detecting defects in battery separators during production. This study initially uses data augmentation techniques in the data processing phase, followed by the utilization of the weighted random sampler method for sampling. Additionally, the dataset is partitioned using the Stratified K-Fold cross-validation method to tackle imbalanced sample data. Subsequently, an ensemble of object detection algorithms involving Faster Region Convolutional Neural Network and RetinaNet is developed. The ensemble method employs a voting mechanism to ascertain the most accurate predictions and utilizes the Adaptive Delta optimization algorithm with adaptive learning rates. This algorithm adjusts the learning rate based on parameter change rates, eliminating the requirement for setting an initial learning rate to ensure result convergence. Finally, a model fine-tuning technique using pre-training transfer learning is applied to improve the detection performance of the ensemble model. Experimental results show that the improved methodology demonstrates a 16.26% increase in recall, a 7.05% improvement in precision, an 11.83% rise in balanced F Score, and a 0.23 increase in the area under the Receiver Operating Characteristic curve. The study results indicate that the proposed method is an effective and accurate approach to detecting defects in lithium-ion battery separators. Full article
(This article belongs to the Special Issue 2D/3D Industrial Visual Inspection and Intelligent Image Processing)
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