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Electronics
Special Issues
Machine Vision for Robotics and Autonomous Systems
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Machine Vision for Robotics and Autonomous Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 336

Special Issue Editors

Dr. Zhenjun Zhang

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: machine vision; robotics and autonomous systems; machine learning
Dr. Xiaodong Bai

E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: machine vision; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lingyun Xiang

E-Mail Website
Guest Editor
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: computer vision; information security; machine learning; autonomous systems

Special Issue Information

Dear Colleagues,

Machine vision is one of the most active research directions in the field of artificial intelligence. It has experienced a remarkable transformation with the advent of powerful deep learning algorithms. These developments have led to breakthroughs in various applications, including object detection and recognition, autonomous navigation, semantic segmentation, behavior analysis, 3D reconstruction, and so on.

We are delighted to invite you to contribute to the Special Issue named "Machine Vision for Robotics and Autonomous Systems" for this journal. This Special Issue aims to provide a platform for showcasing the latest advancements, innovative solutions, and emerging trends in the field of machine vision and its application in robotics and autonomous systems.

Potential topics of interest include, but are not limited to, the following:

  • Robot vision navigation;
  • Vision technology in autonomous systems;
  • Object detection, recognition, and tracking;
  • Image and video semantic segmentation;
  • Human behavior recognition;
  • Image style conversion;
  • Event vision and its applications;
  • Image restoration and enhancement;
  • Generative technologies in the field of vision and multimedia;
  • Application of machine vision in various fields.

Dr. Zhenjun Zhang
Dr. Xiaodong Bai
Prof. Dr. Lingyun Xiang
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. 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
  • robotics and autonomous systems
  • machine learning
  • image analysis

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Published Papers (1 paper)

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Research

33 pages, 7877 KiB  
Article
GDCPlace: Geographic Distance Consistent Loss for Visual Place Recognition
by Shihao Shao and Qinghua Cui
Electronics 2025, 14(7), 1418; https://doi.org/10.3390/electronics14071418 - 31 Mar 2025
Viewed by 190
Abstract
Visual place recognition (VPR) is essential for robots and autonomous vehicles to understand their environment and navigate effectively. Inspired by face recognition, a recent trend for training a VPR model is to leverage classification objective, where the embeddings of images are trained to [...] Read more.
Visual place recognition (VPR) is essential for robots and autonomous vehicles to understand their environment and navigate effectively. Inspired by face recognition, a recent trend for training a VPR model is to leverage classification objective, where the embeddings of images are trained to be similar to corresponding class centers. Ideally, the predicted similarities should be negative correlated to the geographic distances. However, previous studies typically used loss functions from face recognition due to the similarity between the two tasks, which cannot guarantee the rank consistency above as face recognition is unrelated to geographic distance. Current methods for distance-similarity or ordinal constraint are either for sample-to-sample training, only partially meet the constraint, or are incapable for the VPR task. To this end, we provide a mathematical definition geographic distance consistent defining the above consistency that the loss function for VPR should adhere to. Based on it, we derive the upper bound of cross-entropy softmax loss under the desired constraint to minimize, and propose a novel loss function for VPR that is geographic distance consistent, called GDCPlace. To the best of our knowledge, GDCPlace is the first classification loss function designed for VPR. To evaluate our loss, we collected 11 benchmarks that have high domain variability to test on. As our contribution is on the loss function and previous classification-based VPR methods mostly adopt face recognition loss function, we collect several additional loss functions to compare, e.g., loss for face recognition, image retrieval, ordinal classification, and general purpose. The results show that GDCPlace performs the best among different losses and former state-of-the-art (SOTA) for VPR. It is also evaluated for ordinal classification tasks to show the generalizability of GDCPlace. Full article
(This article belongs to the Special Issue Machine Vision for Robotics and Autonomous Systems)
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