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Computer Vision and Machine Learning: Real-World Applications
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Computer Vision and Machine Learning: Real-World Applications

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 September 2026) | Viewed by 2763

Editors

Dr. Chen Feng

E-Mail Website
Guest Editor
School of Electronics, Electrical Engineering and Computer Science, Queen’s University of Belfast, Belfast BT9 5BN, UK
Interests: representation learning; weakly supervised learning; machine learning theory; AI Safety
Dr. Yicong Li

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: data mining; recommendation; graph neural networks; explainable AI
Dr. Zhen Tian

E-Mail Website
Guest Editor
School of Computing Science, The University of Glasgow, Glasgow G12 8RZ, UK
Interests: computer vision; image segmentation; remote sensing; autonomous driving perception; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in computer vision and machine learning have produced models with impressive results on established benchmarks, yet the gap between controlled experimental settings and real-world deployment remains substantial. This Special Issue highlights research that examines how vision and learning systems function under practical constraints—such as domain shift, noisy or imperfect data, resource limitations, and dynamic environments—while also welcoming studies that contribute new algorithms, model designs, and foundational methodologies independent of a specific application context. The aim is to gather work that evaluates or advances accuracy, robustness, reliability, interpretability, efficiency, and long-term stability.

The scope of this collection includes both methodological and applied contributions. Submissions may present new learning paradigms, training strategies, or theoretical analyses; improved algorithms for perception, recognition, or prediction; or integrated multimodal and human-in-the-loop approaches. Application-oriented studies are equally encouraged, including system-level evaluations and deployments in fields such as healthcare, transportation, robotics, manufacturing, security, and environmental monitoring. Work that analyses failure modes, deployment challenges, dataset properties, or ethical and societal considerations is also within scope.

This Special Issue complements the existing literature by bringing together contributions that extend core machine learning and computer vision methods while also addressing their behaviour in practical settings. By assembling diverse methodological innovations and application domains, the collection seeks to strengthen understanding of how learning systems can be designed, evaluated, and applied in ways that are reliable, transparent, and effective across both benchmark environments and real operational contexts.

Dr. Chen Feng
Dr. Yicong Li
Dr. Zhen Tian
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

  • real-world applications
  • robust and reliable AI
  • domain shift
  • noisy or imperfect data
  • multimodal learning
  • resource-constrained and edge computing
  • trustworthy and interpretable AI
  • deployment and system evaluation
  • medical imaging and healthcare AI
  • autonomous driving and transportation
  • robotics and intelligent systems
  • industrial and manufacturing applications
  • environmental monitoring and remote sensing

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

Published Papers (3 papers)

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Research

32 pages, 22420 KB  
Article
FuDensityNet: Occlusion-Aware Multimodal Activation for Robust Object Detection
by Zainab Ouardirhi, Mostapha Zbakh, Mohammed Benjelloun and Sidi Ahmed Mahmoudi
Electronics 2026, 15(13), 2783; https://doi.org/10.3390/electronics15132783 - 24 Jun 2026
Viewed by 454
Abstract
Accurate object detection remains a major challenge in autonomous systems and surveillance, particularly when objects are partially or fully obscured by occlusions. To address this issue, we revisit FuDensityNet as a multimodal detection framework that jointly leverages 2D RGB images and 3D LiDAR [...] Read more.
Accurate object detection remains a major challenge in autonomous systems and surveillance, particularly when objects are partially or fully obscured by occlusions. To address this issue, we revisit FuDensityNet as a multimodal detection framework that jointly leverages 2D RGB images and 3D LiDAR point clouds for robust feature representation. The model integrates spatial and depth cues through low-rank tensor fusion (LRTF) and incorporates an Occlusion Rate (OR) assessment module that estimates the degree of occlusion and dynamically selects the most suitable detection pathway to preserve performance. Experiments on the KITTI and NuScenes datasets indicate that this adaptive strategy improves robustness under high occlusion while maintaining competitive accuracy in less challenging conditions. In particular, FuDensityNet attains 76.6% AP for car detection under “Hard” conditions on KITTI and outperforms several RGB-only and RGB–LiDAR baselines. Owing to its adaptive and modular design, FuDensityNet remains compatible with both 2D and 3D detection pipelines, making it a practical option for real-world environments where visual obstructions are frequent. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning: Real-World Applications)
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25 pages, 3612 KB  
Article
CrtNet: A Cross-Model Residual Transformer Network for Structure-Guided Remote Sensing Scene Classification
by Chaoran Chen, Tianyuan Zhu, Tao Cui, Dalin Li, Adriano Tavares, Yanchun Liang and Yanheng Liu
Electronics 2026, 15(7), 1366; https://doi.org/10.3390/electronics15071366 - 25 Mar 2026
Cited by 1 | Viewed by 854
Abstract
Accurate remote sensing scene classification is essential for large-scale Earth observation but remains challenging due to significant inter-class similarity and complex spatial layouts in medium- and low-resolution imagery. Conventional convolutional neural networks (CNNs) effectively capture local structural patterns but struggle to model long-range [...] Read more.
Accurate remote sensing scene classification is essential for large-scale Earth observation but remains challenging due to significant inter-class similarity and complex spatial layouts in medium- and low-resolution imagery. Conventional convolutional neural networks (CNNs) effectively capture local structural patterns but struggle to model long-range semantic dependencies, whereas Vision Transformers excel at global context modeling yet often show reduced sensitivity to fine-grained spatial structures. To address these limitations, we propose CrtNet, a structure-aware Cross-Model Residual Transformer Network that establishes a dual-stream collaborative architecture integrating convolutional structural representations with Transformer-based semantic modeling through gated residual cross-model interactions. In this framework, a convolutional branch first extracts stable local structural features with strong spatial inductive biases. These features are continuously injected into the Transformer encoding process via residual cross-model connections, enabling persistent structural guidance during global attention modeling. In addition, a sample-adaptive dynamic gating mechanism is introduced to flexibly balance structural and semantic features during prediction. Extensive experiments conducted on two public remote sensing benchmarks, EuroSAT and UCM, demonstrate that CrtNet consistently outperforms representative CNN-based, Transformer-based, and hybrid state-of-the-art models, particularly in visually ambiguous scene categories. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning: Real-World Applications)
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24 pages, 2064 KB  
Article
Meta-Label-Corrected Knowledge Distillation for Partial Multi-Label Learning
by Jiwei Shuai, Can Xu, Haiyan Jiang and Bin Hu
Electronics 2026, 15(6), 1233; https://doi.org/10.3390/electronics15061233 - 16 Mar 2026
Viewed by 770
Abstract
Partial multi-label learning (PML) assigns each instance a candidate label set that contains all relevant labels but may also include irrelevant noisy ones, making reliable disambiguation essential. Although a small number of verified clean labels is often available in practice, existing PML methods [...] Read more.
Partial multi-label learning (PML) assigns each instance a candidate label set that contains all relevant labels but may also include irrelevant noisy ones, making reliable disambiguation essential. Although a small number of verified clean labels is often available in practice, existing PML methods rarely exploit such information to explicitly guide candidate-label correction. Meanwhile, directly applying knowledge distillation (KD) to PML is highly vulnerable to noisy supervision during representation learning, which can aggravate error accumulation under overlapping candidate labels. To address these issues, we propose a meta-guided distillation framework for PML that integrates teacher–student learning with nested meta-optimization. Specifically, the teacher is optimized with large-scale noisy data under the guidance of limited clean labels, so that it can learn calibrated probabilistic label semantics and generate corrected soft targets for student training. To make this meta-correction process scalable, a truncated meta-gradient approximation is further adopted to reduce computational overhead. The resulting corrected teacher outputs are then used to drive robust multi-label distillation for the student. Experiments on multiple benchmark multi-label image datasets demonstrate consistent improvements over seven representative PML methods across standard evaluation metrics. These results show that meta-guided calibration effectively reduces semantic ambiguity and mitigates noise-induced error propagation in partial multi-label learning. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning: Real-World Applications)
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