Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Computer Vision Applied for Industry 4.0
share announcement

Computer Vision Applied for Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 2873

Special Issue Editors

Dr. Bruno Fernandes

E-Mail Website
Guest Editor
Computer Engineering, Universidade de Pernambuco, Recife, Brazil
Interests: computer vision applied for assistive technology; computer vision applied for security; deep learning; reinforcement learning
Dr. Rogério Pontes

E-Mail Website
Guest Editor
Polytechnic School, Universidade de Pernambuco, Recife, Brazil
Interests: mechanical engineering, with an emphasis on manufacturing, reliability of machines and processes; predictive maintenance; quality control

Special Issue Information

Dear Colleagues,

Industry 4.0 represents a disruption to traditional means of production, where production processes have become intelligent and capable of self-configuring to meet the most diverse customer demands.

This fourth industrial revolution has an enormous capacity to increase sustainability, reduce pollution, improve product efficiency, increase production stability, reduce operating costs, and provide the plant with numerous other benefits. Industry 4.0 creates a 'smart factory', which uses data from different sensors to improve processes.

From new technological advances in microelectronics, programming languages, means of communication, and data analysis, it was possible to conceive flexible production means that allow: real-time monitoring of machines and manufacturing plants, analysis of large volumes of data, fast setup of production lines, and communication between equipment.

This new industrial context results from the application of different technologies that are integrated to create specific solutions according to the priority and objective of each organization. However, there are numerous possibilities for combining these technologies to solve industrial problems.

In this context, industry 4.0 is a concept that represents intelligent automation and the integration of different technologies, aiming to promote the digitization of industrial activities, improve processes, and increase productivity.

Due to the significant advances in software and hardware technologies, computer vision systems are increasingly present in the Industry 4.0 scenario due to their excellent performance in the most diverse applications, such as monitoring, measurement, and control, in addition to having good stability and a high level of accuracy.

With the high volume of data available in industrial processes, the application of computer vision can be an excellent alternative capable of detecting and signaling process anomalies, contributing to quick and assertive decision-making, reducing the probability of human errors, and increasing process efficiency.

Therefore, this Special Issue is intended to present new ideas and experimental results in the field of computer vision applied to industry 4.0, from the theory, design, and development to practical applications.

Dr. Bruno Fernandes
Dr. Rogério Pontes
Guest Editors

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. Applied Sciences 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

  • computer vision
  • machine vision
  • Industry 4.0
  • smart manufacturing
  • smart factory
  • process monitoring
  • measurement
  • quality control
  • artificial intelligence
  • big data

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

26 pages, 15478 KiB  
Article
Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
by Nalini Rizkyta Nusantika, Xiaoguang Hu and Jin Xiao
Appl. Sci. 2023, 13(17), 9862; https://doi.org/10.3390/app13179862 - 31 Aug 2023
Viewed by 802
Abstract
Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step [...] Read more.
Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step process involving edge and line detection for PTLI identification. Nonetheless, these traditional methods are often complicated when confronted with challenges such as background noise or variations in illumination, leading to incomplete identification of the target area, missed target regions, or misclassification of background pixels as foreground. This paper proposes a new iced transmission line identification scheme to overcome this limitation. In the initial stage, we integrate the image restoration method with image filter enhancement to restore the image’s color information. This combined approach effectively retains valuable information and preserves the original image quality, thereby mitigating the noise presented during the image acquisition. Subsequently, in the second stage, we introduce an enhanced multi-threshold algorithm to separate background and target pixels. After image segmentation, we enhance the image and obtain the region of interest (ROI) through connected component labeling modification and mathematical morphology operations, eliminating background regions. Our proposed scheme achieves an accuracy value of 97.72%, a precision value of 96.24%, a recall value of 86.22%, and a specificity value of 99.48% based on the average value of test images. Through object segmentation and location, the proposed method can avoid background interference, effectively solve the problem of transmission line icing identification, and achieve 90% measurement accuracy compared to manual measurement on the collected PTLI dataset. Full article
(This article belongs to the Special Issue Computer Vision Applied for Industry 4.0)
►▼ Show Figures

Figure 1

26 pages, 8972 KiB  
Article
Vision Transformers for Anomaly Detection and Localisation in Leather Surface Defect Classification Based on Low-Resolution Images and a Small Dataset
by Antony Douglas Smith, Shengzhi Du and Anish Kurien
Appl. Sci. 2023, 13(15), 8716; https://doi.org/10.3390/app13158716 - 28 Jul 2023
Cited by 5 | Viewed by 1641
Abstract
Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects [...] Read more.
Genuine leather manufacturing is a multibillion-dollar industry that processes animal hides from varying types of animals such as sheep, alligator, goat, ostrich, crocodile, and cow. Due to the industry’s immense scale, there may be numerous unavoidable causes of damages, leading to surface defects that occur during both the manufacturing process and the bovine’s own lifespan. Owing to the heterogenous and manifold nature of leather surface characteristics, great difficulties can arise during the visual inspection of raw materials by human inspectors. To mitigate the industry’s challenges in the quality control process, this paper proposes the application of a modern vision transformer (ViT) architecture for the purposes of low-resolution image-based anomaly detection for defect localisation as a means of leather surface defect classification. Utilising the low-resolution defective and non-defective images found in the opensource Leather Defect detection and Classification dataset and higher-resolution MVTec AD anomaly benchmarking dataset, three configurations of the vision transformer and three deep learning (DL) knowledge transfer methods are compared in terms of performance metrics as well as in leather defect classification and anomaly localisation. Experiments show the proposed ViT method outperforms the light-weight state-of-the-art methods in the field in the aspect of classification accuracy. Besides the classification, the low computation load and low requirements for image resolution and size of training samples are also advantages of the proposed method. Full article
(This article belongs to the Special Issue Computer Vision Applied for Industry 4.0)
►▼ 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