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Applied Sciences
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Applied Computer Vision and Deep Learning
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Applied Computer Vision and Deep Learning

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1013

Special Issue Editors

Dr. Shangshu Yu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: computer vision; deep learning; point cloud processing; visual localization; scene reconstruction; pose estimation; semantic segmentation
Prof. Dr. Peter Peer

E-Mail Website
Guest Editor
Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: computer vision; deep learning; biometrics; deepfake detection; virtual try-on
Special Issues, Collections and Topics in MDPI journals
Dr. Tsung-Jung Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
Interests: visual quality assessment; perceptual image; pattern recognition; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to cutting-edge applications emerging from the fusion of Computer Vision (CV) and Deep Learning (DL). We particularly encourage submissions that demonstrate a powerful synergy between CV and DL to solve complex, real-world problems. The core goal of this issue is to showcase innovations in intelligent perception systems that enable machines to robustly understand and interact with their environments.

We welcome high-quality original research papers on topics including, but not limited to, localization, reconstruction, depth estimation, semantic segmentation, object detection, tracking, recognition, and other vision tasks. Application areas of interest include autonomous driving, robotics, augmented reality, biometrics, and smart manufacturing. We especially value contributions that validate advanced algorithms with real-world data and demonstrate tangible performance gains. This issue aims to provide a platform on which researchers and engineers may exchange forward-thinking, data-driven solutions for building the next generation of autonomous systems.

Dr. Shangshu Yu
Prof. Dr. Peter Peer
Dr. Tsung-Jung Liu
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
  • localization
  • reconstruction
  • object detection
  • semantic segmentation
  • depth estimation
  • tracking
  • recognition

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Published Papers (2 papers)

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Research

28 pages, 2784 KB  
Article
A Statistically Validated and Decoding-Aware CNN–Transformer–CTC Framework for Multi-Font Printed Arabic Word Recognition
by Abderrahime Tabzaoui and Loqman Chakir
Appl. Sci. 2026, 16(9), 4071; https://doi.org/10.3390/app16094071 - 22 Apr 2026
Viewed by 240
Abstract
Printed Arabic Optical Character Recognition (OCR) remains challenging due to complex glyph morphology, typographic variability, and sensitivity to Unicode-preserved evaluation protocols. This work introduces a methodology that explicitly treats decoding strategy and orthographic normalization as primary experimental variables in multi-font Arabic OCR evaluation. [...] Read more.
Printed Arabic Optical Character Recognition (OCR) remains challenging due to complex glyph morphology, typographic variability, and sensitivity to Unicode-preserved evaluation protocols. This work introduces a methodology that explicitly treats decoding strategy and orthographic normalization as primary experimental variables in multi-font Arabic OCR evaluation. A CNN–Transformer encoder trained with Connectionist Temporal Classification (CTC) is employed as a controlled backbone to isolate the effects of inference configuration and text normalization. Through systematic analysis on the APTI benchmark, we demonstrate that decoding policy and diacritic handling significantly influence reported recognition performance. In particular, language-model-guided decoding yields substantial improvements over greedy decoding, while Unicode-preserved evaluation introduces systematic orthographic inflation driven by deterministic diacritic mismatch. These effects are further amplified by strong cross-font variability. The proposed normalization-aware evaluation framework disentangles structural recognition errors from protocol-induced artifacts, providing a more controlled and reproducible basis for Arabic OCR benchmarking. Full article
(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)
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23 pages, 2333 KB  
Article
Measurement of Metal Surface Temperature Based on Visible Light Images: A Strategy for On-Site Image Acquisition
by Xingwang Li, Wenhua Wu, Chengxiang Lei, Yang Chen, Zheng Tian and Qizheng Ye
Appl. Sci. 2026, 16(5), 2556; https://doi.org/10.3390/app16052556 - 6 Mar 2026
Viewed by 330
Abstract
Based on the mechanism of thermally modulated reflected light, visible light images combined with machine learning methods can be used to estimate the surface temperature of metal equipment at ambient temperature under sunlight conditions. However, the surface conditions of on-site equipment and camera [...] Read more.
Based on the mechanism of thermally modulated reflected light, visible light images combined with machine learning methods can be used to estimate the surface temperature of metal equipment at ambient temperature under sunlight conditions. However, the surface conditions of on-site equipment and camera imaging parameters vary greatly across different scenarios, leading to poor generalization of models trained solely on laboratory image databases. To address this, it is necessary to update the original laboratory database by incorporating on-site images and retrain the model accordingly; on the other hand, since most of the on-site equipment is working normally, there are few images capturing fault-induced high temperatures. Even if the method of updating and retraining on-site images is used, the data imbalance in the image database can still cause significant measurement errors in these high-temperature images. This study studies image database update schemes to address both multi-scenario and data imbalance problems and demonstrates that retraining with as little as 5% scenario-specific images or 1% high-temperature images significantly improves temperature prediction accuracy, which was validated through on-site experiments at a substation. By comparing four machine learning algorithms (random forest regression, gradient boosted regression trees, decision trees, and k-nearest neighbors), this study reveals that RFR yields the best performance. These findings enhance the practical applicability of visible light image-based temperature measurement models in engineering contexts. Full article
(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)
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