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Algorithms
Special Issues
Machine-Learning in Computer Vision Applications
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Machine-Learning in Computer Vision Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 22052

Special Issue Editors

Dr. Puneet Sharma

E-Mail Website
Guest Editor
Department of Automation and Process Engineering, UiT-The Arctic University of Norway, Tromsø, Norway
Interests: computer vision; machine learning
Prof. Dr. Fridolin Wild

E-Mail Website
Guest Editor
Institute of Educational Technology, The Open University, Milton Keynes, UK
Interests: extended reality; wearable technologies; performance augmentation; digital education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning is a study of computer algorithms that automatically learn and improve from experience without being explicitly programmed. It is a branch of Artificial Intelligence (AI). Machine learning algorithms are used in a range of disciplines including: computer vision, medical diagnosis, and robot control. This Special Issue features a balance between state-of-the-art research and practical applications within the scope of machine learning and computer vision. We invite you to submit your latest research to this Special Issue, “Machine-learning in computer vision applications”. This Special Issue provides a forum for researchers and practitioners to review and disseminate quality research work and discuss critical issues for further development.

Dr. Puneet Sharma
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. Algorithms 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 1600 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 learning
  • algorithm
  • statistical analysis.

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Published Papers (2 papers)

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Research

11 pages, 15180 KiB  
Article
A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
by Dan Yu, Peng Liu, Dezhi Qiao and Xianglong Tang
Algorithms 2021, 14(6), 188; https://doi.org/10.3390/a14060188 - 21 Jun 2021
Cited by 1 | Viewed by 2437
Abstract
In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of [...] Read more.
In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of the rendezvous and docking safety rule knowledge base by the use of machine learning methods, and the prediction of safety by the use of the base. First, in the ground semi-physical simulation test environment, feature extraction and matching are performed on the images taken by the navigation surveillance camera. Then, the matched features and the rendezvous and docking deviation are used to form training sample pairs, which are further used to construct the safety rule knowledge base by using the decision tree method. Finally, the safety rule knowledge base is used to predict the safety of the subsequent process of the rendezvous and docking based on the current images taken by the surveillance camera, and the probability of success is obtained. Semi-physical experiments on the ground show that the system can improve the level of intelligence in the flight control process and effectively assist ground flight controllers in data monitoring and mission decision-making. Full article
(This article belongs to the Special Issue Machine-Learning in Computer Vision Applications)
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11 pages, 6097 KiB  
Communication
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
by Margrit Kasper-Eulaers, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland and Per Egil Kummervold
Algorithms 2021, 14(4), 114; https://doi.org/10.3390/a14040114 - 31 Mar 2021
Cited by 145 | Viewed by 18158
Abstract
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 [...] Read more.
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection. Full article
(This article belongs to the Special Issue Machine-Learning in Computer Vision Applications)
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