Industrial Machine Learning with Image Technology Integration

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 571

Special Issue Editors


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Department of Systems and Informatics, Setubal School of Technology, Polytechnic Institute of Setubal, Campus do IPS, Estefanilha, 2910-761 Setubal, Portugal
Interests: computer vision; machine (deep) learning; data science; artificial intelligence; industry 4.0/5.0; biomedical image and data analysis; medical imaging
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Department of Information Systems (DSI), School of Engineering, University of Minho, Campus Azurém, 4800–058 Guimarães, Portugal
Interests: 3D computer graphics; virtual reality
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Vision, Interaction and Graphics, Instituto CCG/ZGDV, Universidad del Minho, Campus Azurém, Edificio 14, 4800-058 Guimarães, Portugal
Interests: computer vision; deep learning; computational topology; remote sensing; precision agriculture

Special Issue Information

Dear Colleagues,

The digital transformation of industries is demanding new applications of imaging technologies. Industrial machine learning (ML) with the integration of imaging technologies combines the power of advanced (deep) machine learning algorithms, low-level image processing, and computer vision techniques to enhance various industrial applications. From the agricultural industry to large textile companies, tire production, automotive parts manufacturing, and the panel manufacturing industry, among many others, they are currently using vision systems for building Industry 4.0/5.0-ready applications. This Special Issue requests contributions mainly in machine vision systems and machine (deep) learning algorithms and methods involving tasks such as object detection, image classification, and semantic scene segmentation. Researchers are encouraged to submit their work related to different types of industrial applications, including, but not limited to, the following:

  1. Quality control and inspection;
  2. Predictive maintenance;
  3. Automation and robotics;
  4. Supply chain and inventory management;
  5. Safety and compliance.

Our goal is to foster a comprehensive understanding of the state-of-the-art, encouraging further research and collaboration in this rapidly evolving domain of computer vision, machine (deep) learning, generative AI, and artificial intelligence technologies for industrial applications.

Prof. Dr. Miguel Angel Guevara Lopez
Dr. Luís Gonzaga Mendes Magalhães
Dr. Edel Bartolo Garcia Reyes
Guest Editors

Manuscript Submission Information

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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. Journal of Imaging 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 1800 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

  • computer vision
  • machine (deep) learning
  • generative AI
  • artificial intelligence
  • machine vision
  • Industry 4.0/5.0

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Published Papers (1 paper)

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20 pages, 4395 KiB  
Article
The Creation of Artificial Data for Training a Neural Network Using the Example of a Conveyor Production Line for Flooring
by Alexey Zaripov, Roman Kulshin and Anatoly Sidorov
J. Imaging 2025, 11(5), 168; https://doi.org/10.3390/jimaging11050168 - 20 May 2025
Abstract
This work is dedicated to the development of a system for generating artificial data for training neural networks used within a conveyor-based technology framework. It presents an overview of the application areas of computer vision (CV) and establishes that traditional methods of data [...] Read more.
This work is dedicated to the development of a system for generating artificial data for training neural networks used within a conveyor-based technology framework. It presents an overview of the application areas of computer vision (CV) and establishes that traditional methods of data collection and annotation—such as video recording and manual image labeling—are associated with high time and financial costs, which limits their efficiency. In this context, synthetic data represents an alternative capable of significantly reducing the time and financial expenses involved in forming training datasets. Modern methods for generating synthetic images using various tools—from game engines to generative neural networks—are reviewed. As a tool-platform solution, the concept of digital twins for simulating technological processes was considered, within which synthetic data is utilized. Based on the review findings, a generalized model for synthetic data generation was proposed and tested on the example of quality control for floor coverings on a conveyor line. The developed system provided the generation of photorealistic and diverse images suitable for training neural network models. A comparative analysis showed that the YOLOv8 model trained on synthetic data significantly outperformed the model trained on real images: the mAP50 metric reached 0.95 versus 0.36, respectively. This result demonstrates the high adequacy of the model built on the synthetic dataset and highlights the potential of using synthetic data to improve the quality of computer vision models when access to real data is limited. Full article
(This article belongs to the Special Issue Industrial Machine Learning with Image Technology Integration)
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