Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.0
2.9
Journals
Electronics
Special Issues
Advanced Technologies and Applications for Computer Vision and Recognition Systems
share announcement

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 2832

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-anonymized 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 25598 KB  
Article
WCA-Det: Weak-Class-Aware Dual-Attention Enhancement for Fine-Grained Commodity Detection
by Yixuan Ling, Jinliang Zhang, Yongming Zhang, Mengzi Yang, Kai Xie and Jian-Biao He
Electronics 2026, 15(14), 3047; https://doi.org/10.3390/electronics15143047 - 11 Jul 2026
Viewed by 220
Abstract
Fine-grained commodity detection is a key visual task in intelligent retail, automatic checkout, and warehouse management. Compared with general object detection, commodity detection is more sensitive to subtle packaging differences, similar colors, local text patterns, and class-dependent appearance variations. These factors lead to [...] Read more.
Fine-grained commodity detection is a key visual task in intelligent retail, automatic checkout, and warehouse management. Compared with general object detection, commodity detection is more sensitive to subtle packaging differences, similar colors, local text patterns, and class-dependent appearance variations. These factors lead to unstable recognition for weak classes when a conventional detector is trained directly on a limited product dataset. To address this problem, this study proposes WCA-Det, a weak-class-aware dual-attention enhancement method for fine-grained commodity detection. The detector is built on a YOLO11-based framework and combines efficient multi-scale spatial attention with lightweight channel attention to strengthen discriminative product features. In addition, a weak-class augmentation strategy is introduced during training to improve the robustness of categories with lower mAP50-95, without increasing inference parameters or GFLOPs. Experiments on a self-built 12-class commodity dataset show that the proposed method achieves 0.98640 mAP50 and 0.85535 mAP50-95 at an input size of 640. Compared with YOLO11_EMA, mAP50-95 is improved from 0.84334 to 0.85535, corresponding to a 1.20 percentage-point gain. On the self-built dataset, the final model also outperforms representative YOLO-series detectors, including YOLOv5su, YOLOv8s, YOLOv8m, YOLOv9s, YOLOv10s, and YOLOv10m, while maintaining a compact computational cost. These results indicate that weak-class-aware feature enhancement is effective for fine-grained commodity recognition under practical intelligent retail conditions. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
►▼ Show Figures

Figure 1

31 pages, 5338 KB  
Article
Benchmarking Next-Generation YOLO Architectures for Multi-Platform Forest Fire Recognition
by Iosif Polenakis, Christos Sarantidis and Ioannis Karydis
Electronics 2026, 15(13), 2830; https://doi.org/10.3390/electronics15132830 - 27 Jun 2026
Viewed by 274
Abstract
Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial [...] Read more.
Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial resolution, viewing geometry, and computational constraints present challenges for developing unified detection models. This study presents a comparative benchmarking analysis of the lightweight YOLOv26-nano model for forest fire detection using the FASDD dataset, comprising satellite, UAV, and ground-based imagery. A unified experimental protocol with five-fold cross-validation is adopted to ensure robustness and cross-platform generalization. Performance is enhanced through data augmentation, contrast-limited adaptive histogram equalization, and stochastic gradient descent optimization. Experimental results demonstrate that YOLOv26-nano achieves reliable detection accuracy and demonstrates promising computational characteristics under simulated resource-constrained edge-computing conditions. The proposed benchmarking framework provides a standardized reference for multi-platform fire detection and highlights the suitability of nano-scale object detection models for scalable wildfire monitoring and early-warning systems. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
►▼ Show Figures

Figure 1

26 pages, 5787 KB  
Article
CNS-YOLOv8: An Improved YOLOv8-Based Defect Detection Method
by Runhua Geng, Yuan Jiang, Jin Li, Kaiwen Wu, Yingjian Yang, Ziheng Li and Yaohui Chang
Electronics 2026, 15(12), 2730; https://doi.org/10.3390/electronics15122730 - 21 Jun 2026
Viewed by 275
Abstract
Steel surface defect inspection plays an essential role in maintaining product quality and production safety in industrial manufacturing. However, existing detection methods still encounter difficulties in accurately identifying tiny defects, suppressing interference from complex backgrounds, and balancing detection accuracy with computational cost. To [...] Read more.
Steel surface defect inspection plays an essential role in maintaining product quality and production safety in industrial manufacturing. However, existing detection methods still encounter difficulties in accurately identifying tiny defects, suppressing interference from complex backgrounds, and balancing detection accuracy with computational cost. To address these challenges, this paper proposes CNS-YOLOv8, an improved defect detection model based on YOLOv8n. First, a C2f_SCConv module is introduced to enhance multi-scale feature extraction and spatial representation capability. Second, a Normalization-based Attention Module (NAM) is embedded after the high-level semantic feature layer to improve the model’s sensitivity to critical defect regions. Third, a SlimNeck structure is adopted to strengthen feature fusion while reducing computational overhead. Experimental results on the NEU-DET dataset demonstrate that CNS-YOLOv8 achieves 83.1% mAP@0.5 and 49.6% mAP@0.5:0.95, surpassing YOLOv8n by 3.9 and 1.2 percentage points, respectively. In addition, comparative experiments show that CNS-YOLOv8 outperforms Faster R-CNN and YOLOv7 in terms of mAP@0.5 while requiring substantially fewer GFLOPs. In general, the proposed method balances detection accuracy and computational efficiency effectively, highlighting its potential for real-time industrial surface defect detection. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
►▼ Show Figures

Figure 1

19 pages, 3589 KB  
Article
DIDW-YOLOv11: The Steel Surface Defect Detection Method Based on Improved YOLOv11 Network
by Jiajun Jiang, Yaodan Zhang, Ziyang Xue and Chuzheng Wang
Electronics 2026, 15(12), 2593; https://doi.org/10.3390/electronics15122593 - 12 Jun 2026
Viewed by 265
Abstract
The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel [...] Read more.
The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel surface defect detection model named DIDW-YOLOv11. In the proposed DIDW-YOLOv11, the YOLOv11 C3k2 module is first innovatively improved by C3K2-DIMB, which integrates C3K2 and DIMB by introducing DynamicInceptionDWConv2d (DIDW) to sufficiently strengthen the detailed feature extraction for tiny defects and weak-texture defects, improving the matching degree of multi-scale receptive fields. Then the YOLOv11 SPPF module is enhanced by integrating the IDWFSPPF module for optimizing the fusion of local and global information, which combines average pooling and max pooling to enhance the model’s multi-scale feature fusion capability. An auxiliary detection head (ADH) is finally proposed with an additional coarse loss function to process shallow feature information into the model, which uses extra supervision for shallow features to suppress background noise and reduce false detections. Experimental results on the NEU-DET and GC10-DET datasets show that DIDW-YOLOv11 achieves 4.9% and 3.8% improvements in mAP@0.5 compared to the baseline model YOLOv11s. Our research indicates that DIDW-YOLOv11 exhibits stronger recognition ability and robustness in complex and diverse defect detection, providing an effective solution for steel defect detection in industrial production. In addition, experimental results show that our model offers improved performance over the baseline methods. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
►▼ Show Figures

Figure 1

23 pages, 2910 KB  
Article
MD-YOLO: A Multi-Scale Adaptive and Dual-Attention Enhanced YOLOv11 for Small Object Detection
by Wenyan Zhou and Gu Gong
Electronics 2026, 15(10), 2099; https://doi.org/10.3390/electronics15102099 - 14 May 2026
Cited by 1 | Viewed by 429
Abstract
Recent YOLO-based object detection methods have demonstrated strong performance in real-time applications due to their efficient end-to-end architecture. However, in complex scenarios such as VisDrone2019, existing methods still face limitations in small object detection and multi-scale feature modeling capability. These performance bottlenecks are [...] Read more.
Recent YOLO-based object detection methods have demonstrated strong performance in real-time applications due to their efficient end-to-end architecture. However, in complex scenarios such as VisDrone2019, existing methods still face limitations in small object detection and multi-scale feature modeling capability. These performance bottlenecks are not only attributed to model-level constraints, such as the loss of low-level spatial details during progressive downsampling and the insufficient preservation of fine-grained structural information in high-level semantic representations during feature propagation, which consequently limits multi-scale feature representation and fusion, but are also influenced by data-level factors, including long-tailed distributions and spatial distribution bias. To address these limitations, this paper proposes an improved model named MD-YOLO. First, a Multi-scale Adaptive Channel (MAC) module is introduced into the backbone to replace conventional stride-based downsampling, enhancing multi-scale feature representation while preserving fine-grained information. Second, a Dual Attention Feature Fusion (DAFA) module is designed to align features across different resolutions and further enhance fused representations using both channel and spatial attention mechanisms. Furthermore, a high-resolution P2 detection head is incorporated to enhance the detection capability for dense small objects. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method substantially outperforms the YOLOv11s baseline, improving mAP@0.5 from 38.5% to 45.6% and mAP@0.5:0.95 from 22.8% to 27.1%, while maintaining a reasonable computational cost. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications for Computer Vision and Recognition Systems)
►▼ Show Figures

Figure 1

31 pages, 9682 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 317
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)
►▼ Show Figures

Figure 1

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 564
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop