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Unmanned Vehicle and Industrial Sensors for Internet of Everything

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 1288

Special Issue Editors


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Guest Editor
Department of Technology Entrepreneurship and Innovation, University of Southern Denmark, DK-6400 Sønderborg, Denmark
Interests: computer vision; wireless sensor networks; anomaly detection; extended reality; industrial sensors; digital twins; digitalization and automation

E-Mail Website
Guest Editor
Department of Mathematics & Computer Science, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
Interests: intelligent Internet of Things; computer vision; artificial intelligence; autonomous UAVs; routing; optimization

Special Issue Information

Dear Colleagues,

Unmanned vehicles (UVs) such as UAVs (unmanned aerial vehicles) and UGVs (unmanned ground vehicles) are recognized as useful tools to replace or assist humans in various missions, such as inspection and monitoring, surveillance and transportation, etc. The use of UVs in civilian and defense contexts has significant increased in recent times. Nevertheless, some challenges and open issues remain to ensure the full operational use of UVs.

We are pleased to invite you to contribute to this Special Issue, the aim of which is to present recent advances in technologies and algorithms to improve the levels of autonomy, reliability, and safety of UVs. Topics of interest include, but are not limited to, the following: advanced guidance, path planning, target detection and control algorithms as well as industrial sensors, predictive maintenance, networked swarms, and traffic management to perform field experiments.

Dr. Naeem Ayoub
Prof. Dr. Peter Schneider-Kamp
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.

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Keywords

  • unmanned vehicles
  • machine learning
  • computer vision
  • predictive maintenance
  • industrial sensors
  • extended reality
  • digital twin
  • Internet of Everything

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Published Papers (1 paper)

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Research

19 pages, 4078 KiB  
Article
A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
by Menghao Li, Miao Liu, Weiwei Zhang, Wenfeng Guo, Enqing Chen and Cheng Zhang
Appl. Sci. 2024, 14(16), 7071; https://doi.org/10.3390/app14167071 - 12 Aug 2024
Cited by 1 | Viewed by 966
Abstract
In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory [...] Read more.
In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements. Full article
(This article belongs to the Special Issue Unmanned Vehicle and Industrial Sensors for Internet of Everything)
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