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Electronics
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Machine Learning Applications in Unmanned Aerial Vehicles and Drones
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Machine Learning Applications in Unmanned Aerial Vehicles and Drones

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 1048

Special Issue Editors

Dr. Djedjiga Gigi Belfadel

E-Mail Website
Guest Editor
Electrical Engineering, Fairfield University, Fairfield, CT 06824, USA
Interests: estimation theory; drone navigation; target tracking; signal and image processing; machine; learning; remote sensing applications involving space-based infrared (IR) and electro-optical (EO) sensors
Dr. David A. Haessig

E-Mail Website1 Website2
Guest Editor
Robotics-In-Flight LLC, Montville, NJ 07045, USA
Interests: nonlinear estimation; resilient PNT (pos, nav, timing); secure multi-function waveforms; embedded signal processing; guidance, navigation and control of autonomous and networked UAVs

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integration of machine learning techniques in unmanned aerial vehicles (UAVs) and drone systems, emphasizing innovative research, practical applications, and emerging trends. As UAVs and drones continue to play a crucial role in sectors such as surveillance, agriculture, delivery, infrastructure inspection, and environmental monitoring, machine learning enables autonomy, real-time decision-making, and intelligent behavior in dynamic environments.

We invite high-quality contributions that explore machine learning applications in object and obstacle detection, flight control, route optimization, and swarm coordination. Special attention will be given to the use of onboard sensors, such as cameras, light detection and ranging (LiDAR), radio detection and ranging (radar), global positioning system (GPS), and inertial measurement units (IMUs), to enhance navigation, environment mapping, and situational awareness. Studies addressing challenges related to limited onboard computational resources, real-time processing, and edge artificial intelligence (AI) deployment are also encouraged.

The Special Issue aims to showcase both theoretical advancements and practical implementations of machine learning in UAV systems, particularly those with real-world validation. By bringing together cutting-edge research, this issue seeks to advance intelligent drone technologies and promote the adoption of machine learning for more efficient and autonomous UAV operations across a range of industries.

Dr. Djedjiga Gigi Belfadel
Dr. David A. Haessig
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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning
  • unmanned aerial vehicles (UAVs)
  • obstacle detection
  • autonomous systems
  • drone navigation

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Published Papers (2 papers)

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Research

17 pages, 3599 KB  
Article
Scale-Aware Mosaic Augmentation and GSIoU-Based Varifocal Loss for Robust Object Detection Under Scale Imbalance
by Gyeongseo Kim, Jeonghyeon Kim, Sounghwan Hwang and Han Sol Kim
Electronics 2026, 15(5), 1075; https://doi.org/10.3390/electronics15051075 - 4 Mar 2026
Viewed by 270
Abstract
The performance of deep learning-based object detection methods is heavily dependent on the characteristics of training data. However, real-world detection datasets often exhibit severe imbalance in object-scale distributions, resulting in insufficient supervision for object sizes with limited instances. To address this issue, we [...] Read more.
The performance of deep learning-based object detection methods is heavily dependent on the characteristics of training data. However, real-world detection datasets often exhibit severe imbalance in object-scale distributions, resulting in insufficient supervision for object sizes with limited instances. To address this issue, we propose a scale-aware mosaic augmentation algorithm and a generalized scale-adaptive intersection over union (GSIoU)-based varifocal loss (VFL) function. The proposed scale-aware mosaic augmentation method alleviates object-scale imbalance by explicitly regulating scale distributions during training sample construction, overcoming the tendency of conventional mosaic augmentation to preserve inherent dataset imbalance. Furthermore, to handle the limitation of existing IoU-based quality targets that impose relatively large penalties for small objects, we replace the localization quality target in VFL with GSIoU, thereby enabling more consistent classification performance across object scales. We evaluate the effectiveness of the proposed method by applying the proposed method to RT-DETRv2 and conducting experiments on the HRSC2016-MS dataset. Experimental results demonstrate that the proposed method improves the overall average precision (AP) by 1.11AP over the baseline, with a particularly notable improvement of 16.43AP in small-object average precision, confirming that the proposed approach achieves balanced detection performance across object scales. Full article
(This article belongs to the Special Issue Machine Learning Applications in Unmanned Aerial Vehicles and Drones)
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21 pages, 5491 KB  
Article
A Low-Cost UAV-Based Computer Vision Pipeline for Public Space Measurement: The Case of Sesquilé, Colombia
by Pedro Fernando Melo Daza, Rodrigo Cadena Martínez, Cristian Lozano Tafur, Iván Felipe Rodríguez Baron and Jaime Enrique Orduy
Electronics 2026, 15(5), 923; https://doi.org/10.3390/electronics15050923 - 25 Feb 2026
Viewed by 321
Abstract
Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a [...] Read more.
Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article
(This article belongs to the Special Issue Machine Learning Applications in Unmanned Aerial Vehicles and Drones)
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