Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Applied Computer Vision and Deep Learning

Print Special Issue Flyer
Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4535

Share This 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-anonymized 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

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 (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

23 pages, 22415 KB

Open AccessArticle

Lightweight Gated Parallel Fusion of CoordAtt and ASPP for Degradation-Robust Monocular Parking-Line Segmentation

by Yanhong Ning, Shugang Liu and Da Yan

Appl. Sci. 2026, 16(16), 8203; https://doi.org/10.3390/app16168203 - 18 Aug 2026

Viewed by 352

Abstract

Existing vision-based parking slot perception methods predominantly adopt Around View Monitor (AVM) or Bird’s Eye View (BEV) imagery and target intact markings. Robust segmentation of heavily worn parking lines from monocular rear-facing cameras, however, remains largely unexplored. This paper proposes a lightweight fully convolutional network for degradation-robust monocular parking-line segmentation. Coordinate Attention (CoordAtt) and Atrous Spatial Pyramid Pooling (ASPP) operate in parallel at the encoder bottleneck, and their outputs are fused through a learnable spatial-adaptive gate that assigns pixel-wise weights to each branch, eliminating the mutual interference inherent in serial fusion. On a self-constructed dataset of 2273 annotated monocular parking-line images, the proposed network achieves 64.03% IoU and 280.5 FPS with 7.65 M parameters. Ablation experiments confirm that serial fusion degrades directional precision, whereas parallel gated fusion preserves both directional cues and multi-scale context through pixel-level adaptive allocation. These results demonstrate improved segmentation accuracy and wear-completion robustness under severe marking degradation, providing an efficient perception solution for monocular camera-based automatic parking systems. Full article

(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)

► Show Figures

Figure 1

22 pages, 118312 KB

Open AccessArticle

SiStNet: A Single-Stage Convolutional Neural Network for Vehicle Detection

by Yashar Azadvatan and Murat Kurt

Appl. Sci. 2026, 16(13), 6476; https://doi.org/10.3390/app16136476 - 29 Jun 2026

Viewed by 926

Abstract

In this study, we propose SiStNet, a single-stage deep learning architecture for vehicle detection in autonomous driving scenarios. The proposed architecture is trained entirely from scratch on domain-specific data without relying on pretrained backbones and is evaluated against representative baseline detectors under identical training conditions. Experiments are conducted on the KITTI dataset under a consistent training and evaluation protocol. An ablation study conducted under a reduced training budget (20% of data, 30 epochs) revealed that multi-scale detection, data augmentation, and anchor-based prediction did not contribute positively to detection performance under the given training conditions. Based on these findings, the final SiStNet architecture was simplified by removing these three components and re-trained under the full training budget. The resulting model achieves a mean Average Precision (mAP) of

0.5033 \pm 0.0072

and a recall of

0.6935 \pm 0.0214

, representing substantial improvements over the initially reported values (

0.3896

and

0.439

, respectively). The inference speed of SiStNet is

24.41 \pm 0.02

FPS, which satisfies the real-time threshold of 20 FPS defined in this study. The model achieves lower mAP than baseline detectors that employ larger and deeper architectures; all models were trained from scratch under identical conditions, so the accuracy gap reflects architectural capacity differences rather than pretraining advantages. SiStNet is presented as a domain-specific, scratch-trained alternative that achieves competitive detection performance without reliance on large-scale pretraining, at the cost of lower mAP relative to deeper baselines. Full article

(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)

► Show Figures

Figure 1

21 pages, 2436 KB

Open AccessArticle

A Multi-Stage Cross-Modal Fusion Framework for Visible–Infrared Object Detection

by Anfu Zhu, Yinbing Chen, Heng Guo, Zhizeng Zhang, Yaning Yang, Qinghua Jiang, Yueyong Li and Yi Yang

Appl. Sci. 2026, 16(12), 5771; https://doi.org/10.3390/app16125771 - 8 Jun 2026

Cited by 1 | Viewed by 508

Abstract

In visible–infrared object detection under complex environments, cross-modal fusion often suffers from spatial misalignment, semantic inconsistency, and unstable feature responses under varying illumination conditions. To address these issues, this paper proposes a dual-branch visible–infrared object detection framework based on YOLOv11. A staged refine–interact–modulate cross-modal fusion structure (RIFN) is designed to progressively stabilize the cross-modal fusion process. Specifically, the Cross-Modal Spatial–Channel Calibration (CSK) module first suppresses cross-modal discrepancies, the bidirectional Cross-Modal Synergy (CSM) module subsequently performs complementary interaction modeling, and the Complementary Modulation (CM) module further recalibrates fused representations to improve fusion stability under complex environments. Extensive experiments conducted on the public LLVIP and M³FD datasets demonstrate the effectiveness and generalization capability of the proposed framework. Compared with DEYOLO, the proposed method improves mAP@50 by 2.4% on the M³FD dataset while reducing GFLOPs by 2.4, demonstrating superior computational efficiency. In addition, compared with the Transformer-based GM-DETR, the proposed framework improves mAP@50 by 2.1% on the LLVIP dataset and increases inference speed by 210 FPS, achieving higher detection accuracy together with better inference efficiency. Experimental results demonstrate that the proposed staged cross-modal fusion strategy can effectively enhance the robustness of visible–infrared object detection under complex illumination conditions and background interference. Full article

(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)

► Show Figures

Figure 1

28 pages, 2784 KB

Open AccessArticle

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 568

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. 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)

► Show Figures

Figure 1

23 pages, 2333 KB

Open AccessArticle

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

Cited by 1 | Viewed by 556

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

31 pages, 8807 KB

Open AccessReview

Visible–Infrared Image Fusion for Computer Vision: A Review of Datasets and Fusion Strategies in Object Detection and Facial-Expression Recognition

by Muhammad Tahir Naseem, Chan-Su Lee and Muhammad Adnan Khan

Appl. Sci. 2026, 16(13), 6757; https://doi.org/10.3390/app16136757 - 6 Jul 2026

Viewed by 755

Abstract

Visible and infrared (IR) image fusion has become an important strategy for improving computer vision performance under low illumination, occlusion, and some poor-visibility conditions. By integrating complementary textural information from visible images with thermal or IR cues, VIR fusion can enhance object localization, detection robustness, and facial-expression recognition (FER). This review examines VIR fusion techniques and datasets for computer vision applications, with object detection (OD) considered as a relatively mature scene-level task and FER considered as an emerging human-centered application. It summarizes major multimodal datasets, compares early-fusion approaches, including sensor- and feature-level fusion, with late-fusion approaches, including score- and decision-level fusion, and discusses representative machine learning and deep learning methods. The review also evaluates commonly used performance metrics and identifies current limitations, including dataset imbalance, sensor misalignment, limited demographic diversity in facial-expression datasets, computational complexity, and weak real-time generalization. Finally, key application areas, including surveillance, healthcare, remote sensing, autonomous systems, and human–computer interaction, are discussed. This review highlights the need for better-aligned multimodal datasets, standardized evaluation protocols, lightweight fusion architectures, and robust models capable of operating in dynamic real-world environments. Full article

(This article belongs to the Special Issue Applied Computer Vision and Deep Learning)

► Show Figures

Journal Menu

Journal Browser

Applied Computer Vision and Deep Learning

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI