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Electronics
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Advanced Technologies and Applications for Computer Vision and Recognition Systems
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Advanced Technologies and Applications for Computer Vision and Recognition Systems

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 492

Special Issue Editors

Dr. Fei Wu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: pattern recognition; computer vision; multimodal learning and applications
Special Issues, Collections and Topics in MDPI journals
Dr. Xiwei Dong

E-Mail Website
Guest Editor
School of Computer and Big Data Science, Jiujiang University, Jiujiang 332000, China
Interests: image recognition and classification; pattern recognition; computer vision; pattern classification; machine learning
Dr. Songsong Wu

E-Mail Website
Guest Editor
School of Computer Science, Guangdong University of Petrochemical Technology, Maoming 525000, China
Interests: computer vision; multimedia; transfer learning

Special Issue Information

Dear Colleagues,

The rapid development of artificial intelligence, sensing devices and high-performance computing has greatly advanced computer vision and recognition systems. Massive visual, textual and networked data are continuously generated from cameras, mobile devices, social media and cyber–physical infrastructures such as intelligent transportation and smart cities. How to efficiently process these heterogeneous data, learn powerful representations and build reliable recognition systems under constraints of real time, privacy, security and robustness has become an important research topic, involving not only visual perception but also areas such as encrypted traffic analysis and the detection of misleading or fake information.

This Special Issue, “Advanced Technologies and Applications for Computer Vision and Recognition Systems,” focuses on new theories, algorithms and system architectures that enhance perception and recognition capabilities in such scenarios. We welcome submissions on deep learning and transformer-based models for vision, lightweight and efficient architectures, multimodal and cross-modal learning and robust or explainable recognition methods suitable for real deployments. Research topics include image and video understanding, object detection and tracking, scene analysis, edge computing and edge intelligence for vision applications, recognition of complex signals in networked environments such as encrypted traffic anomaly detection, image generation, vision-based fault diagnosis as well as multimedia and multimodal fake news or misinformation detection. Both fundamental studies and application-oriented work supported by experiments or case studies fall within the scope of this Special Issue.

Dr. Fei Wu
Dr. Xiwei Dong
Dr. Songsong Wu
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

  • computer vision
  • pattern recognition
  • multimodal learning
  • cross-modal perception
  • edge computing
  • edge intelligence
  • encrypted traffic anomaly detection
  • network behavior analysis
  • fake news detection

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Published Papers (2 papers)

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Research

29 pages, 2270 KB  
Article
A Heterogeneous Modular Framework for Pre-Trained Image Dehazing Models Based on Haze Level Clustering
by Cheng-Hsiung Hsieh, Xin-Rui Lin, Wei-Cheng Liao and Yung-Fa Huang
Electronics 2026, 15(8), 1676; https://doi.org/10.3390/electronics15081676 - 16 Apr 2026
Viewed by 88
Abstract
While pre-trained deep learning models have significantly advanced image dehazing, their restoration performance often fluctuates substantially across varying haze densities, leading to inconsistent performance across diverse atmospheric conditions. To address this limitation, this study introduces a performance analysis approach based on Haze Image [...] Read more.
While pre-trained deep learning models have significantly advanced image dehazing, their restoration performance often fluctuates substantially across varying haze densities, leading to inconsistent performance across diverse atmospheric conditions. To address this limitation, this study introduces a performance analysis approach based on Haze Image Clustering (HIC) to systematically evaluate the specialized strengths of various state-of-the-art models within specific haze-level intervals. Building upon these evaluations, we propose a heterogeneous modular framework equipped with a dynamic switching mechanism that adaptively activates the optimal pre-trained module for each detected haze level. Extensive experiments conducted on the OTS and ODF benchmark datasets demonstrate that while individual models exhibit regional performance drops, the proposed framework consistently maintains superior performance across all haze intensities. Quantitative results indicate that the proposed modular network achieves a significant PSNR improvement of up to 6.946 dB compared to DehazeFlow. Furthermore, regarding the no-reference Dehazing Quality Index (DHQI), our framework attains a top score of 68.448, surpassing the best individual baseline. These findings validate that the proposed strategy effectively enhances both restoration fidelity and visual naturalness without the need for additional training or fine-tuning, offering a robust and computationally efficient solution for real-world image dehazing. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
23 pages, 1950 KB  
Article
Encrypted Traffic Detection via a Federated Learning-Based Multi-Scale Feature Fusion Framework
by Yichao Fei, Youfeng Zhao, Wenrui Liu, Fei Wu, Shangdong Liu, Xinyu Zhu, Yimu Ji and Pingsheng Jia
Electronics 2026, 15(8), 1570; https://doi.org/10.3390/electronics15081570 - 9 Apr 2026
Viewed by 251
Abstract
With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address [...] Read more.
With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address this challenge, this paper proposes FMTF, a Multi-Scale Feature Fusion method based on Federated Learning for encrypted traffic anomaly detection. FMTF constructs graph structures at three scales—spatial, statistical, and content—to comprehensively characterize traffic features. At the spatial scale, communication graphs are constructed based on host-to-host IP interactions, where each node represents the IP address of a host and edges capture the communication relationships between them. The statistical scale builds traffic statistic graphs based on interactions between port numbers, with nodes representing individual ports and edge weights corresponding to the lengths of transmitted packets. At the content scale, byte-level traffic graphs are generated, where nodes represent pairs of bytes extracted from the traffic data, and edges are weighted using pointwise mutual information (PMI) to reflect the statistical association between byte occurrences. To extract and fuse these multi-scale features, FMTF employs the Graph Attention Network (GAT), enhancing the model’s traffic representation capability. Furthermore, to reduce raw-data exposure in distributed edge environments, FMTF integrates a federated learning framework. In this framework, edge devices train models locally based on their multi-scale traffic features and periodically share model parameters with a central server for aggregation, thereby optimizing the global model without exposing raw data. Experimental results demonstrate that FMTF maintains efficient and accurate anomaly detection performance even under limited computing resources, offering a practical and effective solution for encrypted traffic identification and network security protection in edge computing environments. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
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