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Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1739

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Editors

Dr. Ciprian Orhei

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Guest Editor

Department of Communications, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University of Timișoara, Bd. Vasile Parvan, Nr. 2, 300223 Timisoara, Romania
Interests: computer vision; image processing; pattern recognition; image enhancement; embedded & GPU vision systems; advanced driver assistance systems (ADAS); urban landmark detection
Special Issues, Collections and Topics in MDPI journals

Dr. Muguras Mocofan

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Guest Editor

Dr. Radu Vasiu

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Guest Editor

Institute for Digital Transformation, Politehnica University of Timisoara, Piata Victoriei, Nr. 2, 300006 Timișoara, Romania
Interests: multimedia technologies; audio–video compression; enhanced learning technologies; blockchain technologies; AR/VR technologies; smart city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Computer Vision has emerged as a key enabler of intelligent information systems, transforming the way we process and interpret visual data. These technologies play a fundamental role in extracting structured information from unstructured image and video sources, supporting a wide range of applications.

This Special Issue provides a platform for researchers and practitioners to share recent developments, innovative algorithms, and applied solutions that advance the integration of Computer Vision into multimedia technologies. We welcome contributions that demonstrate how vision systems enhance multimedia experiences—whether through content understanding, dynamic interaction, visual augmentation, or intelligent streaming.

Applied contributions are encouraged, particularly those that explore the intersection of information theory and Computer Vision, specific to multimedia systems, such as intelligent video streaming, real-time content adaptation, semantic video summarization, interactive AR/VR environments, vision-based user interaction, and multimedia data fusion in IoT or smart city platforms.

Topics of interest include, but are not limited to, the following:

Advanced algorithms for multimedia content enhancement and restoration (e.g., image/video enhancement, denoising, super-resolution).
Visual data compression and transmission methods optimized for multimedia streaming platforms.
Real-time multimedia systems and embedded vision processing solutions for multimedia applications.
Vision-based dynamic texture analysis, synthesis, and classification for multimedia applications.
Computer Vision integration with AR/VR multimedia systems.
Semantic understanding, scene interpretation, and content indexing for multimedia retrieval systems.
AI-driven multimedia analytics, including deep learning approaches for multimedia understanding.
Vision-enabled multimedia applications for smart cities and urban environments (e.g., urban landmark detection, multimedia surveillance).
Multimedia vision systems leveraging IoT networks and sensor fusion.
Multimedia systems supporting advanced e-learning, training, and interactive communication.

We invite researchers and practitioners to contribute original research articles and reviews that advance the state of the art in vision-based information technologies.

Dr. Ciprian Orhei
Dr. Muguras Mocofan
Dr. Radu Vasiu
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. Information is an international peer-reviewed open access monthly 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 1800 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
image processing
multimedia systems
smart city multimedia systems
multimedia IoT systems
object detection
pattern recognition
image enhancement
deep learning
embedded vision
smart applications

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Published Papers (4 papers)

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Research

25 pages, 2439 KB

Open AccessArticle

GAD-YOLO: A Multi-Level Feature Enhancement Network for Dense Small Traffic Object Detection in Intelligent Transportation Systems

by Yuan He, Xing Li, Junfa Zhu, Lina Zhang, Dengqi Yang and Xiaowei Li

Information 2026, 17(8), 797; https://doi.org/10.3390/info17080797 - 19 Aug 2026

Viewed by 132

Abstract

Dense small traffic object detection is essential for intelligent transportation systems but remains challenging because distant targets contain limited visual details, densely distributed objects frequently overlap, and complex road backgrounds introduce substantial interference. To address these limitations, this study proposes GAD-YOLO, a multi-level feature enhancement network based on YOLOv9. Ghost-MSConv performs lightweight multi-receptive-field feature extraction in the backbone, Mixed Local Channel Attention combines local spatial relationships with global channel dependencies during feature refinement, and DySample performs content-adaptive point sampling during feature upsampling. In the primary experiments on a six-class traffic object dataset derived from UA-DETRAC, GAD-YOLO achieved a precision of 78.9%, a recall of 76.4%, an

{mAP}_{50}

of 82.8%, and an

{mAP}_{50 : 95}

of 65.5%. Compared with YOLOv9c, precision, recall,

{mAP}_{50}

, and

{mAP}_{50 : 95}

increased by 5.4, 0.5, 3.1, and 4.8 percentage points, respectively. Under the complexity statistics used in the primary experiments, GAD-YOLO contains 25.455 M parameters and requires 102.4 GFLOPs, compared with 25.442 M parameters and 103.2 GFLOPs for YOLOv9c. Additional experiments on the public VisDrone2019-DET benchmark were conducted to evaluate cross-dataset applicability, small-object performance, scene-density sensitivity, and standardized inference efficiency. On the VisDrone2019-DET test-dev set, GAD-YOLO improved

{mAP}_{50}

and

{mAP}_{50 : 95}

from 26.5% and 15.7% to 27.1% and 16.3%, respectively. A COCO-style analysis further showed that

{AP}_{S}

increased from 6.72% to 7.31%, while the dense-subset

{mAP}_{50 : 95}

increased from 13.95% to 14.44%. Under an RTX 4090, batch-size-one,

640 \times 640

, FP32 inference protocol, GAD-YOLO achieved a mean latency of 9.98 ms and a throughput of 100.20 FPS. These results show that GAD-YOLO improves the primary traffic object detection task and yields modest positive performance differences on an independent public benchmark under the fixed experimental setting, while maintaining real-time inference capability. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems)

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34 pages, 29088 KB

Open AccessArticle

GhostNetV2-YOLO: A Lightweight Detector for Multi-View Aesthetic Object Detection in Home Environments

by Kaiwen Qiu, Yixuan Tu, Xin Zhou, Yiting Wang, Yiqun Tan and Wenquan Huang

Information 2026, 17(8), 781; https://doi.org/10.3390/info17080781 - 14 Aug 2026

Viewed by 171

Abstract

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems)

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20 pages, 8485 KB

Open AccessArticle

LCA-Net: A Lightweight Network for Small Object Detection in Road Traffic Scenes

by Shan Lin, Bensheng Yun, Zhenyu Lin, Jie Shen and Qinghua Xu

Information 2026, 17(8), 724; https://doi.org/10.3390/info17080724 - 27 Jul 2026

Viewed by 308

Abstract

Detecting small and distant objects in road traffic scenarios remains challenging owing to limited pixel resolution, cluttered backgrounds, and resource constraints on edge computing platforms. This work presents LCA-Net, a computationally efficient framework for small object detection that balances accuracy with model complexity. The framework incorporates three complementary designs: an Adaptive Deformable Downsampling Module (ADDM) that merges asymmetric and deformable convolution operations to improve spatial feature encoding while explicitly accounting for the parameter and computational cost of offset and modulation-mask prediction; a Cross-Scale Feature Fusion Pyramid (CSFFP) specifically engineered for minute objects, which augments multi-scale feature learning and enhances detection of far-field small targets; and a Lightweight Feature-Gated Detection Head (LFGDH) that employs channel–spatial attention to selectively emphasize informative features, thereby reducing both parameter count and computational cost. On Udacity, LCA-Net improves mAP@0.5 by 2.3 percentage points; on VisDrone2019, it improves mAP@0.5 by 1.7 percentage points. Across both benchmarks, the complete model reduces the parameter count by 25.58% and GFLOPs by 16.05% relative to YOLOv8-N. On the RTX A6000, LCA-Net-N reduces forward-pass latency from 1.82 to 1.63 ms, increases throughput from 549 to 613 FPS, and lowers peak GPU memory from 1180 to 1015 MiB. These results demonstrate a favorable accuracy–efficiency trade-off for real-time traffic perception. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems)

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22 pages, 3642 KB

Open AccessArticle

A Deployment-Oriented Case Study of YOLO-Based Model Compression for On-Board Space Debris Detection

by Liam Kerr and Ognjen Arandjelović

Information 2026, 17(7), 650; https://doi.org/10.3390/info17070650 - 3 Jul 2026

Viewed by 563

Abstract

Space debris presents a growing operational risk to spacecraft, especially in low Earth orbit, where collisions can generate further debris and increase future collision probability. Active debris removal and in-orbit servicing require robust close-range perception, but on-board systems are constrained by power, memory, processing capability and the need for reliable real-time operation. This paper investigates convolutional object detection for on-board space debris detection using the SPARK 2022 spacecraft detection dataset. A YOLOv3 detector is fine-tuned and used to evaluate post-training compression through static quantisation and pruning. A lightweight architectural variant, YOLO-DWSC, is also introduced by replacing the YOLOv3-tiny backbone convolutions with depthwise separable convolutions while retaining the detection head. The full-precision YOLOv3 model achieves 0.972 mAP₅₀ and 0.884 mAP_50:95, while 8-bit static quantisation reduces model size from 405 MB to 102 MB with only a small reduction in mAP₅₀, although tighter localisation accuracy is more affected. YOLO-DWSC is much smaller and faster, reaching 256.4 FPS on the tested GPU at 43 MB, but with reduced accuracy. We present this work as a controlled case study rather than an attempt at state-of-the-art SPARK 2022 performance. The original challenge test labels were unavailable, and the experiments therefore use a class-balanced re-split of the labelled data. The results should consequently be interpreted as internally controlled comparisons of compression behaviour, not as leaderboard-comparable benchmark results. Pruning and a two-pass refinement method are also evaluated. The results indicate that simple compression methods can be useful for broad region-of-interest detection, but they also show that claims about on-board deployment require caution. Speed benefits are hardware- and runtime-dependent, and safety-critical proximity operations require evaluation criteria better aligned with full-object containment. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing: Technologies and Applications for Multimedia Systems)

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