Object Detection and Image Classification

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

Deadline for manuscript submissions: 10 August 2024 | Viewed by 897

Special Issue Editors


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Guest Editor
School of Computing & Communications, The Open University, Walton Hall, Kents Hill, Milton Keynes MK7 6AA, UK
Interests: image processing; object detection and tracking; computer vision; automatic umpiring; anomaly detection; deepfake detection

E-Mail Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
Interests: computing, simulation and modelling; human factors; industrial automation; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
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Special Issue Information

Dear Colleagues,

Rapid advances in machine learning and artificial intelligence in the last decade have enabled various objects in images to be effectively identified and classified. This advancement makes the detection of objects in various application domains possible, such as detecting cancerous cells in microscopic images, classifying plants and insects in natural environments, identifying astronomical objects in space and distinguinshing deepfake images from real ones. In some cases, these detections are more accurate than human experts’. However, various challenges still need to be resolved before automatic object objection applications can be widely deployed. These challenges includes improved detection accuracy and reliability, explainability and acceptability.

This Special Issue invites high-quality papers that present novel ideas in object detection and classification, the explanation of detection decision and the improvement on acceptability in any application domains. Areas relevant to this Special Issue include, but are not limited to, the following:

  • Object detection and tracking;
  • Classification of images;
  • Deepfake detection;
  • Explainable AI on object detection;
  • Object localization in images;
  • Augmented reality;
  • Autonomous vehicles and robots;
  • Umpire Decision Review System;
  • Remote sensing;
  • Disease detection and diagnosis;
  • Biometrics.

Dr. Patrick Wong
Prof. Dr. Yifan Zhao
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

  • object detection
  • object tracking
  • image classification
  • deepfake detection
  • explainable AI
  • object localization
  • augumented reality
  • automonous vehicles
  • automonous robots
  • umpire decision review system
  • remote sensing
  • disease detection
  • biometrics

Published Papers (1 paper)

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Research

17 pages, 9871 KiB  
Article
Vision AI System Development for Improved Productivity in Challenging Industrial Environments: A Sustainable and Efficient Approach
by Changmo Yang, JinSeok Kim, DongWeon Kang and Doo-Seop Eom
Appl. Sci. 2024, 14(7), 2750; https://doi.org/10.3390/app14072750 - 25 Mar 2024
Viewed by 421
Abstract
This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside [...] Read more.
This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images. Additionally, the proposed architecture for sustainable AI system development included cropping the inspection part images to minimize the technology development time, investment costs, and the reuse of images. The algorithm was retrained using mixed learning data to maintain and improve its performance in industrial fields. This AI system development architecture effectively addresses the challenges faced in applying AI technology at industrial sites and was demonstrated through experimentation and application. Full article
(This article belongs to the Special Issue Object Detection and Image Classification)
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