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Electronics
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Applications and Challenges in Computer Vision, Pattern Recognition, and Image...
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Applications and Challenges in Computer Vision, Pattern Recognition, and Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2253

Special Issue Editor

Dr. Yeong Jun Koh

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, Chungnam National University, Daejeon 34134, Korea
Interests: computer vision; image processing; machine learning

Special Issue Information

Dear Colleagues,

We have the pleasure to invite you to submit research articles to the Special Issue, “Applications and Challenges in Computer Vision, Pattern Recognition, and Image Processing”. This Special Issue focuses on a wide range of applications in computer vision, pattern recognition, and image processing. We are particularly interested in all aspects of applications from low-level and high-level techniques of computer vision and image processing based on machine learning, pattern recognition, and deep learning. We welcome submissions addressing practical issues in the real world and novel aspects in computer vision and image processing challenges.

Topics of interest for this Special Issue include but are not limited to:

  • Object recognition;
  • Segmentation;
  • Image understanding;
  • Object tracking and motion analysis;
  • Face and pose;
  • Low-level vision;
  • Image enhancement;
  • Video processing and analysis;
  • Deep learning models in computer vision and image processing;
  • Applications in vehicles, robotics, surveillance, medical images, and industrial inspection.

Dr. Yeong Jun Koh
Guest Editor

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. Electronics 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
  • image processing
  • deep learning
  • pattern recognition
  • machine learning
  • artificial intelligence
  • image and video analysis

Published Papers (1 paper)

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Research

17 pages, 8885 KiB  
Article
Small Target Detection Algorithm for UAV Aerial Photography Based on Improved YOLOv5s
by Jingcheng Shang, Jinsong Wang, Shenbo Liu, Chen Wang and Bin Zheng
Electronics 2023, 12(11), 2434; https://doi.org/10.3390/electronics12112434 - 27 May 2023
Cited by 5 | Viewed by 1761
Abstract
At present, UAV aerial photography has a good prospect in agricultural production, disaster response, and other aspects. The application of UAVs can greatly improve work efficiency and decision-making accuracy. However, owing to inherent features such as a wide field of view and large [...] Read more.
At present, UAV aerial photography has a good prospect in agricultural production, disaster response, and other aspects. The application of UAVs can greatly improve work efficiency and decision-making accuracy. However, owing to inherent features such as a wide field of view and large differences in the target scale in UAV aerial photography images, this can lead to existing target detection algorithms missing small targets or causing incorrect detections. To solve these problems, this paper proposes a small target detection algorithm for UAV aerial photography based on improved YOLOv5s. Firstly, a small target detection layer is applied in the algorithm to improve the detection performance of small targets in aerial images. Secondly, the enhanced weighted bidirectional characteristic pyramid Mul-BiFPN is adopted to replace the PANet network to improve the speed and accuracy of target detection. Then, CIoU was replaced by Focal EIoU to accelerate network convergence and improve regression accuracy. Finally, a non-parametric attention mechanism called the M-SimAM module is added to enhance the feature extraction capability. The proposed algorithm was evaluated on the VisDrone-2019 dataset. Compared with the YOLOV5s, the algorithm improved by 7.30%, 4.60%, 5.60%, and 6.10%, respectively, in mAP@50, mAP@0.5:0.95, the accuracy rate (P), and the recall rate (R). The experiments show that the proposed algorithm has greatly improved performance on small targets compared to YOLOv5s. Full article
(This article belongs to the Special Issue Applications and Challenges in Computer Vision, Pattern Recognition, and Image Processing)
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