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Information
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Intelligent Image Processing by Deep Learning
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Intelligent Image Processing by Deep Learning

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

Deadline for manuscript submissions: 27 September 2024 | Viewed by 356

Special Issue Editors

Prof. Dr. Haiyan Li

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: artificial intelligence; pattern recognition; image processing
Prof. Dr. Dongming Zhou

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: artificial intelligence; pattern recognition; image processing

Special Issue Information

Dear Colleagues,

Image processing plays a crucial role in various domains, including computer vision, information analysis, and multimedia applications. With the advent of deep learning and artificial intelligence techniques, there is a growing interest in developing intelligent image processing systems that can effectively analyze, enhance, and interpret images. However, challenges like Dataset Limitations, Interpretability and Explainability, Computational Complexity and many other issues still exit that need to be addressed for further progress and widespread adoption. This Special Issue aims to provide a platform for researchers and practitioners to explore the advancements and applications of deep learning-based approaches in the field of image processing. Authors are encouraged to present novel deep learning-based methodologies, algorithms, and frameworks that contribute to the advancement of intelligent image processing. Submissions may include theoretical contributions, experimental evaluations, and practical applications. We welcome original research papers, survey articles, and systematic literature reviews that address the challenges and opportunities in the field of intelligent image processing using deep learning. Manuscripts should demonstrate the effectiveness, efficiency, and applicability of the proposed methods. We invite original research articles, case studies, and reviews that address the related topics such as:

  1. Image inpainting and restoration using deep learning techniques;
  2. Advanced image analysis and understanding;
  3. Information processing and analysis in images;
  4. Computer vision algorithms and applications;
  5. Machine learning for image processing and analysis;
  6. Intelligent image processing system design and implementation;
  7. Multimodal target monitoring and tracking techniques;
  8. Multimodal image fusion and enhancement approaches;
  9. Application and case studies of deep learning in image processing.

Prof. Dr. Haiyan Li
Prof. Dr. Dongming Zhou
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. 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 1600 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

  • image processing
  • image inpainting
  • image analysis
  • information processing
  • information analysis
  • computer vision
  • deep learning
  • machine learning
  • application and case studies
  • intelligent image processing system
  • multimodal target monitoring and tracking
  • multimodal image fusion
  • artificial intelligence

Published Papers (1 paper)

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Research

15 pages, 6946 KiB  
Article
MCF-YOLOv5: A Small Target Detection Algorithm Based on Multi-Scale Feature Fusion Improved YOLOv5
by Song Gao, Mingwang Gao and Zhihui Wei
Information 2024, 15(5), 285; https://doi.org/10.3390/info15050285 - 17 May 2024
Abstract
In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To [...] Read more.
In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To address the aforementioned issues, we propose the small object algorithm model MCF-YOLOv5, which has undergone three improvements based on YOLOv5. Firstly, a data augmentation strategy combining Mixup and Mosaic is used to increase the number of small targets in the image and reduce the interference of noise and changes in detection. Secondly, in order to accurately locate the position of small targets and reduce the impact of unimportant information on small targets in the image, the attention mechanism coordinate attention is introduced in YOLOv5’s neck network. Finally, we improve the Feature Pyramid Network (FPN) structure and add a small object detection layer to enhance the feature extraction ability of small objects and improve the detection accuracy of small objects. The experimental results show that, with a small increase in computational complexity, the proposed MCF-YOLOv5 achieves better performance than the baseline on both the VisDrone2021 dataset and the Tsinghua Tencent100K dataset. Compared with YOLOv5, MCF-YOLOv5 has improved detection APsmall by 3.3% and 3.6%, respectively. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)
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