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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics, 2nd...
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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1271

Special Issue Editors

Dr. Vladimir László Tadić

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, Electrical Engineering and Computer Science, Technical College of Applied Sciences in Zrenjanin, Đorđa Stratimirovića 23, 23000 Zrenjanin, Serbia
2. John von Neumann Faculty of Informatics, Obuda University, Becsi ut 96/B., 1034. Budapest, Hungary
3. Symbolic Methods in Material Analysis and Tomography Research Group, Faculty of Engineering and Information Technology, University of Pecs, Boszorkany Str. 6, H-7624 Pecs, Hungary
Interests: image and signal processing; computer vision; robot vision; fuzzy logic and soft computing; deep learning; artificial intelligence; biomedicine and biomatics; electronics; telecommunications; measurement technologies; computer engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peter Odry

E-Mail Website
Guest Editor
1. Institute of Information Technology, University of Dunaujvaros, Tancsics Mihaly u. 1/A Pf.: 152, 2401 Dunaujvaros, Hungary
2. Symbolic Methods in Material Analysis and Tomography Research Group, Faculty of Engineering and Information Technology, University of Pecs, Boszorkany Str. 6, H-7624 Pecs, Hungary
Interests: robotics; fuzzy control; electrical engineering; optimization methods; electrical impedance tomography; control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to present volume II of this Special Issue to highlight the current Advances in Image Processing, Artificial Intelligence and Intelligent Robotics. For decades, scientists and engineers have been striving to develop digital image processing systems that match the efficiency of the human visual system. Recently, artificial intelligence, deep learning and soft computing methods have been involved in the development of various sophisticated image processing algorithms. Further, image processing plays a significant role in intelligent robotics, where the goal is the realization of precise, robust and intelligent control solutions based on image information. Thus, the use of vision sensors and cameras in robotics has inspired the deployment of effective applications in industry, the automotive industry, smart cities, agriculture, biology, medicine, etc.

The aim of this Special Issue is to give researchers the opportunity to provide new trends and the latest achievements and research directions as well as present their current work on important problems in image processing, deep learning, biomatics and biomedicine, soft computing, sensor fusion, robotic vision, the automotive industry and applied industrial solutions in robotics.

In this Special Issue, original research articles, short communications, technical reports, perspectives, extended conference papers and reviews are welcome. Research areas may include (but are not limited to) the following:

  • 2D and 3D image processing;
  • image segmentation and texture analysis;
  • image filtering, restoration and enhancement;
  • biomedical image processing;
  • biomatics and applied artificial intelligence;
  • pattern recognition and shape detection;
  • deep learning and vision transformers;
  • soft computing and fuzzy techniques;
  • sensor fusion and measurements;
  • robot vision;
  • intelligent and applied robotics;
  • hardware and architectures for image processing and robotics;
  • smart cities;
  • automotive industry and electric vehicles technology;
  • remote sensing.

Dr. Vladimir László Tadić
Prof. Dr. Peter Odry
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 250 words) can be sent to the Editorial Office for assessment.

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

  • image and signal processing
  • medical imaging and biomatics
  • artificial intelligence
  • deep learning and vision transformers
  • soft computing
  • fuzzy logic
  • sensor fusion
  • measurements
  • robotic vision
  • industrial robotics
  • electric vehicles

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Related Special Issue

Published Papers (3 papers)

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19 pages, 700 KB  
Article
BiGRMT: Bidirectional GRU–Recurrent Memory Transformer for Efficient Long-Sequence Anomaly Detection in High-Concurrency Microservices
by Ruicheng Zhang, Renzun Zhang, Shuyuan Wang, Kun Yang, Miao Xu, Dongwei Qiao and Xuanzheng Hu
Electronics 2025, 14(23), 4754; https://doi.org/10.3390/electronics14234754 - 3 Dec 2025
Viewed by 140
Abstract
In high-concurrency distributed systems, log data often exhibits sequence uncertainty and redundancy, which pose significant challenges to the accuracy and efficiency of anomaly detection. To address these issues, we propose BiGRMT, a hybrid architecture that integrates Bidirectional Gated Recurrent Unit (Bi-GRU) with a [...] Read more.
In high-concurrency distributed systems, log data often exhibits sequence uncertainty and redundancy, which pose significant challenges to the accuracy and efficiency of anomaly detection. To address these issues, we propose BiGRMT, a hybrid architecture that integrates Bidirectional Gated Recurrent Unit (Bi-GRU) with a Recurrent Memory Transformer (RMT). BiGRMT enhances local temporal feature extraction through bidirectional modeling and adaptive noise filtering using Bi-GRU, while a RMT component is incorporated to significantly extend the model’s capacity for long-sequence modeling via segment-level memory. The Transformer’s multi-head attention mechanism continues to capture global time dependencies but now with improved efficiency due to the RMT’s memory-sharing design. Extensive experiments on three benchmark datasets from LogHub (Spark, BGL(Blue Gene/L), and HDFS (Hadoop distributed file system)) demonstrate that BiGRMT achieves strong results in terms of precision, recall, and F1-score. It attains a precision of 0.913, outperforming LogGPT (0.487) and slightly exceeding Temporal logical attention network (TLAN) (0.912). Compared to LogPal, which prioritizes detection accuracy, BiGRMT strikes a better balance by significantly reducing computational overhead while maintaining high detection performance. Even under challenging conditions such as a 50% increase in log generation rate or 20% injected noise, BiGRMT maintains F1-scores of 87.4% and 83.6%, respectively, showcasing excellent robustness. These findings confirm that BiGRMT is a scalable and practical solution for automated fault detection and intelligent maintenance in complex distributed software systems. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics, 2nd Edition)
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18 pages, 7181 KB  
Article
Research on a Method for Recognizing Text on Book Spines in Libraries Based on Improved YOLOv11 and Optimized PaddleOCR
by Zheng Li, Bingzhen Guo and Dengcong Mu
Electronics 2025, 14(23), 4689; https://doi.org/10.3390/electronics14234689 - 28 Nov 2025
Viewed by 271
Abstract
The growing scale of libraries necessitates intelligent management solutions, particularly for book inventory tasks. To address the challenge of book spine recognition in dense, text-heavy environments, this study proposes an integrated approach combining an enhanced YOLOv11 model with a hyperparameter-optimized PaddleOCR framework. The [...] Read more.
The growing scale of libraries necessitates intelligent management solutions, particularly for book inventory tasks. To address the challenge of book spine recognition in dense, text-heavy environments, this study proposes an integrated approach combining an enhanced YOLOv11 model with a hyperparameter-optimized PaddleOCR framework. The methodology involves augmenting the YOLOv11 object detector with a Channel-Spatial Dual Attention Mechanism (CBAM) to better extract spine texture features and suppress interference from adjacent books. For the text recognition stage, PaddleOCR’s hyperparameters were task-optimized by adopting the RecAug data augmentation strategy, adjusting the curved text detection loss weight, expanding the character dictionary, and modifying the input image size. Experimental results on a self-constructed Book Spine Dataset show that the improved YOLOv11 achieved a segmentation accuracy of 97.4%, a 2.1% increase over the baseline, while reducing computational load and parameters. The optimized PaddleOCR saw its character error rate drop from 8.6% to 3.2%. Consequently, the end-to-end system attained a 96.8% single-book recognition accuracy in real bookshelf scenarios, demonstrating that this targeted strategy significantly enhances performance for intelligent library management. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics, 2nd Edition)
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Other

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57 pages, 2889 KB  
Systematic Review
AI-Based Weapon Detection for Security Surveillance: Recent Research Advances (2016–2025)
by Thangavel Murugan, Nasurudeen Ahamed Noor Mohamed Badusha, Amnah Rashed Obaid Ali Semaihi, Maryam Mohamed Rashed Alkindi, Eman Mohammed Rashed Alnaqbi and Ghala Hmouda Turki Alketbi
Electronics 2025, 14(23), 4609; https://doi.org/10.3390/electronics14234609 - 24 Nov 2025
Viewed by 595
Abstract
The necessity for intelligent monitoring has grown more urgent as the number of crimes involving firearms and knives in homes and public areas has increased. Traditional CCTV systems require human operators, whose attentiveness and the impracticability of monitoring multiple video feeds simultaneously limit [...] Read more.
The necessity for intelligent monitoring has grown more urgent as the number of crimes involving firearms and knives in homes and public areas has increased. Traditional CCTV systems require human operators, whose attentiveness and the impracticability of monitoring multiple video feeds simultaneously limit their effectiveness. Artificial intelligence (AI)-based vision systems can automatically detect firearms and enhance public safety, thereby overcoming this constraint. In accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) criteria, a systematic evaluation of AI-based weapon detection for security monitoring is conducted. The paper summarizes research works on AI, machine learning, and deep learning techniques for identifying weapons in surveillance footage from 2016 to 2025, encompassing 101 research papers. The reported precision ranged from 78% to 99.5%, recall ranged from 83% to 97%, and mean average precision (mAP) ranged from approximately 70% to 99%. While AI-based monitoring significantly enhances detection accuracy, issues with inconsistent evaluation criteria, limited real-world validation, and dataset variability persist. The research study emphasizes the need for uniform benchmarking, robust privacy protections, and standardized datasets to ensure the ethical and reliable implementation of AI-driven weapon-detection systems. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics, 2nd Edition)
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