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Latest Research on Computer Vision and Its Application
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Latest Research on Computer Vision and Its Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1479

Special Issue Editors

Prof. Dr. Zhenshen Qu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: intelligent unmanned systems; computer vision
Prof. Dr. Guopu Zhu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: multimedia forensics and security; AI security; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue explores the latest advancements in computer vision and its expanding role in solving real-world challenges. Inspired by interdisciplinary innovations in deep learning, autonomous systems and AI-driven edge devices, this issue bridges theoretical research with real-world implementation. For instance, visual SLAM systems have dramatically improved localization accuracy and autonomy for unmanned vehicles in GPS-denied environments. In industrial manufacturing lines, automated visual inspection can identify tiny surface flaws in products with superhuman accuracy and efficiency. By showcasing both theoretical breakthroughs and industry-driven case studies, this special issue welcomes contributions that demonstrate recent advances in computer vision and its applications. The call covers but not limits to a wide range of areas including visual perception and reconstruction, industrial visual inspection, vision-language models and applications, AI-driven edge computing, and innovative advances in the field.

Prof. Dr. Zhenshen Qu
Prof. Dr. Guopu Zhu
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. Applied Sciences 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
  • deep learning
  • vision-language models
  • visual perception
  • autonomous systems
  • visual inspection
  • AI-driven edge computing

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Published Papers (3 papers)

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Research

17 pages, 16338 KB  
Article
AmpFormer: Amplitude-Aware Spectral Recalibration for Shadow Removal
by Lianmeng Wei and Sihui Luo
Appl. Sci. 2026, 16(9), 4118; https://doi.org/10.3390/app16094118 - 23 Apr 2026
Viewed by 209
Abstract
Recent years have witnessed significant progress in deep learning-based shadow removal. However, most prior methods operate primarily in the spatial domain or rely on coarse frequency cues, while the informative role of amplitude components in the frequency domain remains largely unexplored. The amplitude [...] Read more.
Recent years have witnessed significant progress in deep learning-based shadow removal. However, most prior methods operate primarily in the spatial domain or rely on coarse frequency cues, while the informative role of amplitude components in the frequency domain remains largely unexplored. The amplitude spectrum encodes spectral energy that reflects global illumination and fine texture that strongly influence shadow appearance. Motivated by this observation, we propose AmpFormer, a U-shaped transformer architecture that explicitly models amplitude information for robust shadow correction. Central to AmpFormer is a lightweight SFR module inserted at each encoder–decoder stage: SFR extracts multi-scale amplitude cues from compact spectral representations, learns per-channel adaptive gains and subtle phase adjustments, and injects the recalibrated frequency features into the spatial stream. To further encourage amplitude-aware restoration, we introduce an amplitude loss that explicitly regularizes spectral energy with emphasis on global illumination consistency. Extensive experiments on standard benchmarks demonstrate that AmpFormer achieves state-of-the-art restoration quality while offering a favorable computational-efficiency-accuracy trade-off, validating the practical benefit of amplitude-aware frequency modeling for shadow removal. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Its Application)
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36 pages, 7711 KB  
Article
Integrating Visual Perception with Conservative Enhanced Bio-Inspired Optimization for Safe UAV Trajectory Planning
by Qiushuang Gao, Zhenshen Qu, Qihang Zhang and Yuhao Shang
Appl. Sci. 2026, 16(7), 3245; https://doi.org/10.3390/app16073245 - 27 Mar 2026
Viewed by 303
Abstract
Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original [...] Read more.
Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Its Application)
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18 pages, 608 KB  
Article
TDI-SF: Trustworthy Dynamic Inference via Uncertainty-Gated Retrieval and Similarity-Gated Strict Fallback
by Yiyi Xu, Siyuan Li, Zhouxiang Yu, Jiahao Hu and Pengfei Liu
Appl. Sci. 2026, 16(4), 2023; https://doi.org/10.3390/app16042023 - 18 Feb 2026
Viewed by 341
Abstract
Retrieval-time augmentation can correct hard test samples but may also introduce harmful interference when retrieved neighbors are unreliable. We propose TDI-SF (trustworthy dynamic inference via similarity-gated strict fallback), a safety-oriented dynamic inference strategy that intervenes only when needed and falls back to a [...] Read more.
Retrieval-time augmentation can correct hard test samples but may also introduce harmful interference when retrieved neighbors are unreliable. We propose TDI-SF (trustworthy dynamic inference via similarity-gated strict fallback), a safety-oriented dynamic inference strategy that intervenes only when needed and falls back to a frozen baseline when retrieval quality is insufficient. Uncertainty-gated selective retrieval triggers on a hard subset, defined by high entropy or low margin predictions (q=0.3), and similarity-gated fusion weights neighbor evidence by maximum similarity with a strict fallback threshold (alpha-mode=maxsim, min_maxsim). We evaluate on ImageNet-100 (ResNet-50) and CICIDS2017 (MLP) and report overall accuracy, hard-subset accuracy, calibration, negative flips, and risk–coverage behavior alongside efficiency. Comprehensive evaluation under both clean and degraded retrieval conditions demonstrates the value of each component. On ImageNet-100, TDI-SF improves hard-subset accuracy by 0.92% and overall accuracy by 0.30%, applying retrieval to only 32.6% of samples with 1.38 ms overhead per triggered sample. On CICIDS2017, the same mechanism yields +1.30% hard-subset gains with only 0.43 ms/hard overhead. These results show a simple, auditable recipe for safer retrieval-augmented inference across heterogeneous domains. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Its Application)
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