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Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 675

Special Issue Editors

Prof. Dr. Youngchul Bae

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Guest Editor
Division of Electrical and Computer Engineering, Chonnam National University, Daehak-ro 50, Yeosu 59626, Republic of Korea
Interests: intelligent system; deep learning; chaotic dynamics; nonlinear control; energy prediction; fuzzy and neural network; robot control; digital twins and CPS (cyber–physical system)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Suk-Seung Hwang

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Guest Editor
Department of Electronic Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of Korea
Interests: adaptive signal processing; wireless communications; location detection technology; interference cancellation; channel estimation; GPS; RFID
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will consist of selected high-quality papers from the 24th ISIS (The 24th International Symposium on Advanced Intelligent Systems), which will be held in Gwangju, Republic of Korea, from the 6th to the 9th December 2023. This international conference is designed to explore a wide variety of ideas. Contributors will be invited to submit and present papers concerning “intelligent systems and the soft computing”. Topics of selected papers will include various sensor techniques, devices, and applications for intelligent systems. These papers are subjected to peer review and are published so as to widely disseminate new research results, including developments and applications.

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

  • Vision and sensors.
  • Sensing and communications.
  • Sensors information fusion.
  • Sensing for artificial intelligence, neural networks, neuro-fuzzy systems, chaotic systems, big data analysis, learning and adaptive systems.
  • Human–computer interaction and interface based on sensors.
  • Fault detection and diagnosis, embedded real-time systems, and intelligent transportation systems based on sensors.
  • Sensing for intelligent control and robotics, intelligent manufacturing systems, mechatronics design.

Prof. Dr. Youngchul Bae
Prof. Dr. Suk-Seung Hwang
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. Sensors 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 2600 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

  • sensors
  • intelligent system
  • robotics
  • soft computing and its applications
  • artificial intelligence

Published Papers (1 paper)

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Research

30 pages, 6907 KiB  
Article
Research on the Multiple Small Target Detection Methodology in Remote Sensing
by Changman Zou, Wang-Su Jeon and Sang-Yong Rhee
Sensors 2024, 24(10), 3211; https://doi.org/10.3390/s24103211 - 18 May 2024
Viewed by 311
Abstract
This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement [...] Read more.
This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS))
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