Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Machine Vision in Industrial Systems
share announcement

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 2477

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 5142 KiB  
Article
Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms
by Yifan Lei and Degang Xu
Electronics 2024, 13(15), 2905; https://doi.org/10.3390/electronics13152905 - 23 Jul 2024
Viewed by 809
Abstract
In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, [...] Read more.
In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process. Full article
(This article belongs to the Special Issue Machine Vision in Industrial Systems)
►▼ Show Figures

Figure 1

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
Cited by 1 | Viewed by 1243
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop