Innovative Technologies and Services for Unmanned Aerial Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 771

Special Issue Editors

School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Interests: communication and networking; swarm intelligence and cooperative UAVs; autonomous flight systems

E-Mail Website
Guest Editor
Mobile Technology Research Department, China Telecom Research Institute, Beijing 102209, China
Interests: Internet of Things; vehicular networks; massive-MIMO precoding; artificial intelligence; UAV communication; channel estimation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue on "Innovative Technologies and Services for Unmanned Aerial Vehicles" in Electronics. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology with a wide range of applications in various domains. This Special Issue aims to explore the latest developments in UAV technologies and services, highlighting innovative solutions that enhance their capabilities and applications.

The purpose of this Special Issue is to provide a platform for researchers, practitioners, and industry experts to share their knowledge, experiences, and perspectives on UAV technology. We aim to foster interdisciplinary discussions and collaborations to push the boundaries of UAV applications. By bringing together high-quality research articles and reviews, we seek to create a comprehensive overview of the advancements in this field and stimulate further research and development.

The scope of this Special Issue encompasses a broad range of topics related to innovative technologies and services for UAVs. We encourage submissions in areas including, but not limited to:

  • Autonomous navigation and control systems: advanced algorithms and techniques for autonomous UAV navigation, obstacle detection and avoidance, path planning, and formation flying.
  • Sensing and imaging technologies: integration of novel sensing technologies such as hyperspectral imaging, LiDAR, thermal imaging, and their applications in UAV data acquisition.
  • Communication and networking: efficient communication and networking solutions for UAVs operating in networked environments, including reliable and secure communication, network coordination, and swarm intelligence.
  • Payload and service delivery: innovative payload designs, delivery mechanisms, and applications of UAVs in payload transportation and service delivery, such as medical supply delivery or infrastructure inspections.
  • Energy efficiency and sustainability: research on energy harvesting, optimization algorithms, and sustainable power sources for UAVs to improve energy efficiency and sustainability.
  • Regulatory and legal aspects: discussions on the legal frameworks, privacy concerns, safety regulations, and ethical considerations associated with UAV operations.

We welcome original research articles, reviews, and case studies that address these topics. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of the contributions. Accepted papers will be published in this Special Issue, contributing to the collective knowledge and understanding of innovative technologies and services for UAVs.

We look forward to receiving your high-quality contributions to this Special Issue. Together, we can advance the field of UAV technology and its applications.

Dr. Tao Hong
Dr. Fei Qi
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. 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

  • unmanned aerial vehicles (UAVs)
  • control systems
  • sensing technologies
  • wireless communication
  • optimization algorithms

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 9284 KiB  
Article
An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles
by Qi Yu, Ao Liu, Xinxin Yang and Weimin Diao
Electronics 2024, 13(9), 1781; https://doi.org/10.3390/electronics13091781 - 5 May 2024
Viewed by 392
Abstract
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, [...] Read more.
Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
Show Figures

Figure 1

Back to TopTop