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Electronics
Special Issues
Machine Vision in Industrial Systems
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Machine Vision in Industrial Systems

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 518

Special Issue Editor

Prof. Dr. Degang Xu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410010, China
Interests: machine vision; intelligent control of industrial systems

Special Issue Information

Dear Colleagues,

In the era of Industry 4.0, machine vision has emerged as a pivotal element in transforming industrial systems into intelligent and digital entities. It plays a crucial role in enhancing productivity, ensuring quality, and bolstering the capabilities of industrial automation, safety, and surveillance systems. Our Special Issue aims to gather and discuss techniques for designing and optimizing machine vision algorithms in industrial automation systems. This scope includes areas such as image acquisition and processing, machine learning, deep learning, etc.

Furthermore, our focus extends to exploring the practical applications of machine vision in industrial automation, such as product quality inspection, production process monitoring, automated warehouse management, and industrial robot navigation. Moreover, the integration of machine vision with the industrial internet, artificial intelligence, embedded systems, and other fields will be explored to promote digitalization and intelligence in industrial production.

This Special Issue invites contributions that span a wide array of topics, including, but not limited to, the following subjects:

  • Artificial intelligence;
  • Machine learning;
  • Deep learning;
  • Machine vision;
  • Intelligent manufacturing and digital factory;
  • Product quality inspection;
  • Production process monitoring;
  • Industrial robot navigation and positioning;
  • Robot vision recognition technology.

Prof. Dr. Degang Xu
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • machine vision
  • deep learning
  • intelligent manufacturing
  • quality inspection
  • process monitoring
  • robot vision
  • autonomous robot

Published Papers (1 paper)

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Research

11 pages, 1571 KiB  
Article
Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network
by Jianhua Liu, Shiyi Jiang, Zhongmei Wang and Jiahao Liu
Electronics 2024, 13(11), 2201; https://doi.org/10.3390/electronics13112201 - 5 Jun 2024
Viewed by 287
Abstract
Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance [...] Read more.
Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data. Full article
(This article belongs to the Special Issue Machine Vision in Industrial Systems)
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