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Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 10853

Special Issue Editors

Dr. Claudio Piciarelli

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
Interests: computer vision; pattern recognition; machine learning; deep learning; sensor reconfiguration; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Dr. Danilo Avola

E-Mail Website
Guest Editor
Department of Computer Science, Sapienza University of Rome, 00185 Rome, Italy
Interests: computer vision (feature extraction and pattern analysis); scene and event understanding (by people and/or vehicles and/or objects); human–computer interaction (pose estimation and gesture recognition by hands and/or body); sketch-based interaction (handwriting and freehand drawing); human–behaviour recognition (actions, emotions, feelings, affects, and moods by hands, body, facial expressions, and voice); biometric analysis (person re-identification by body visual features and/or gait and/or posture/pose); artificial intelligence (machine/deep learning); medical image analysis (MRI, ultrasound, X-rays, PET, and CT); multimodal fusion models; brain–computer interfaces (interaction and security systems); signal processing; visual cryptography (by RGB images); smart environments and natural interaction (with and without virtual/augmented reality); robotics (monitoring and surveillance systems with PTZ cameras, UAVs, AUVs, rovers, and humanoids)
Special Issues, Collections and Topics in MDPI journals
Dr. Alessio Mecca

E-Mail Website
Guest Editor
Department of Computer Science, Sapienza University of Rome, 00185 Rome, Italy
Interests: biometrics; gait recognition; signal processing; signal comparison/matching; computer vision; skeleton-based feature extraction; person identification; robotics; monitoring and surveillance with UAVs; anomaly detection; ultrasound imaging; few-shot object detection
Dr. Marco Cascio

E-Mail Website
Guest Editor
Department of Computer Science, Sapienza University of Rome, 00185 Rome, Italy
Interests: computer vision; feature extraction; pattern analysis; scene and event understanding; anomaly detection; anomaly localization; visual analysis of image and/or video; human behaviour recognition; biometric analysis; artificial intelligence; machine/deep learning; multimodal fusion models; signal processing; person re-identification; radio biometric signatures; image synthesis through Wi-Fi signals; robotics; PTZ cameras; UAVs; AUVs; rovers; humanoids

Special Issue Information

Dear Colleagues,

The recent widespread use of Unmanned Aerial Vehicles (UAVs) as well as their reduced costs allow their application in real-life remote sensing scenarios. For this reason, several of the tasks that are usually performed by humans could now benefit from an UAV-based approach, e.g., to operate in hazardous environments, automatize tedious tasks, or even just improve system performance. Moreover, UAVs can equip different kinds of cameras, such as RGB, depth RGB, and/or infrared, which can be useful for different application contexts. Different from traditional techniques, which often require monitoring different locations, there is no need to duplicate the acquisition devices, drastically reducing costs and allowing a better-quality camera of the same price. In addition, in some specific fields, the use of UAVs can have a high impact. For example, in environmental anomaly detection, old techniques were based on satellite acquisitions. However, the high distance of the acquisition source could hinder the detection of small objects. In surveillance, the use of UAVs can help to cover large areas with less effort with respect to the normal cameras that can only cover fixed locations. Examples of other possible applications are environmental data acquisition, smart agriculture, surveillance, monitoring of dangerous areas, search-and-rescue operations, anomaly detection, etc.

This Special Issue is looking for new solutions for tasks that can be eased by using UAVs, novel deep network architectures that are especially focused on UAV-captured videos, and possible new tasks on which the use of UAVs can be beneficial. 

For this Special Issue, we welcome the most recent advancements that are related but not limited to:

  • Anomaly detection using UAV videos;
  • Persons recognition using UAV videos or images;
  • Person re-identification using UAV videos or images;
  • Surveillance strategies using UAV videos;
  • New techniques for map reconstruction using UAV videos or images;
  • Novel strategies for combining simultaneous videos from more than one UAV;
  • Novel adaptive strategies to automatize UAV exploration according to an unknown environment;
  • Solutions for novel tasks exploiting UAV videos and images.

Dr. Claudio Piciarelli
Dr. Danilo Avola
Dr. Alessio Mecca
Dr. Marco Cascio
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. Remote Sensing 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 2700 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

  • UAV
  • anomaly detection
  • surveillance
  • person re-identification
  • drone
  • deep learning
  • video/image processing

Published Papers (6 papers)

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Research

23 pages, 36072 KiB  
Article
Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification
by Guoqing Zhang, Tianqi Liu and Zhonglin Ye
Remote Sens. 2024, 16(5), 775; https://doi.org/10.3390/rs16050775 - 22 Feb 2024
Viewed by 582
Abstract
In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few [...] Read more.
In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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20 pages, 6621 KiB  
Article
Unsupervised Joint Contrastive Learning for Aerial Person Re-Identification and Remote Sensing Image Classification
by Guoqing Zhang, Jiqiang Li and Zhonglin Ye
Remote Sens. 2024, 16(2), 422; https://doi.org/10.3390/rs16020422 - 22 Jan 2024
Viewed by 1049
Abstract
Unsupervised person re-identification (Re-ID) aims to match the query image of a person with images in the gallery without the use of supervision labels. Most existing methods usually generate pseudo-labels through clustering algorithms for contrastive learning, which inevitably results in noisy labels assigned [...] Read more.
Unsupervised person re-identification (Re-ID) aims to match the query image of a person with images in the gallery without the use of supervision labels. Most existing methods usually generate pseudo-labels through clustering algorithms for contrastive learning, which inevitably results in noisy labels assigned to samples. In addition, methods that only apply contrastive learning at the clustering level fail to fully consider instance-level relationships between instances. Motivated by this, we propose a joint contrastive learning (JCL) framework for unsupervised person Re-ID. Our proposed method involves creating two memory banks to store features of cluster centroids and instances and applies cluster and instance-level contrastive learning, respectively, to jointly optimize the neural networks. The cluster-level contrastive loss is used to promote feature compactness within the same cluster and reinforce identity similarity. The instance-level contrastive loss is used to distinguish easily confused samples. In addition, we use a WaveBlock attention module (WAM), which can continuously wave feature map blocks and introduce attention mechanisms to produce more robust feature representations of a person without considerable information loss. Furthermore, we enhance the quality of our clustering by leveraging camera label information to eliminate clusters containing single camera captures. Extensive experimental results on two widely used person Re-ID datasets verify the effectiveness of our JCL method. Meanwhile, we also used two remote sensing datasets to demonstrate the generalizability of our method. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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13 pages, 5389 KiB  
Communication
Synthetic Aperture Anomaly Imaging for Through-Foliage Target Detection
by Rakesh John Amala Arokia Nathan and Oliver Bimber
Remote Sens. 2023, 15(18), 4369; https://doi.org/10.3390/rs15184369 - 05 Sep 2023
Cited by 1 | Viewed by 1337
Abstract
The presence of foliage is a serious problem for target detection with drones in application fields such as search and rescue, surveillance, early wildfire detection, or wildlife observation. Visual as well as automatic computational methods, such as classification and anomaly detection, fail in [...] Read more.
The presence of foliage is a serious problem for target detection with drones in application fields such as search and rescue, surveillance, early wildfire detection, or wildlife observation. Visual as well as automatic computational methods, such as classification and anomaly detection, fail in the presence of strong occlusion. Previous research has shown that both benefit from integrating multi-perspective images recorded over a wide synthetic aperture to suppress occlusion. In particular, commonly applied anomaly detection methods can be improved by the more uniform background statistics of integral images. In this article, we demonstrate that integrating the results of anomaly detection applied to single aerial images instead of applying anomaly detection to integral images is significantly more effective and increases target visibility as well as precision by an additional 20% on average in our experiments. This results in enhanced occlusion removal and outlier suppression, and consequently, in higher chances of detecting targets that remain otherwise occluded. We present results from simulations and field experiments, as well as a real-time application that makes our findings available to blue-light organizations and others using commercial drone platforms. Furthermore, we outline that our method is applicable for 2D images as well as for 3D volumes. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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17 pages, 8269 KiB  
Article
Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery
by Loucif Hebbache, Dariush Amirkhani, Mohand Saïd Allili, Nadir Hammouche and Jean-François Lapointe
Remote Sens. 2023, 15(5), 1218; https://doi.org/10.3390/rs15051218 - 22 Feb 2023
Cited by 3 | Viewed by 2335
Abstract
Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our [...] Read more.
Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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21 pages, 3390 KiB  
Article
Estimation of Human Body Height Using Consumer-Level UAVs
by Andrea Tonini, Marco Painho and Mauro Castelli
Remote Sens. 2022, 14(23), 6176; https://doi.org/10.3390/rs14236176 - 06 Dec 2022
Cited by 2 | Viewed by 1757
Abstract
Consumer-level UAVs are often employed for surveillance, especially in urban areas. Within this context, human recognition via estimation of biometric traits, like body height, is of pivotal relevance. Previous studies confirmed that the pinhole model could be used for this purpose, but only [...] Read more.
Consumer-level UAVs are often employed for surveillance, especially in urban areas. Within this context, human recognition via estimation of biometric traits, like body height, is of pivotal relevance. Previous studies confirmed that the pinhole model could be used for this purpose, but only if the accurate distance between the aerial camera and the target is known. Unfortunately, low positional accuracy of the drones and the difficulties of retrieving the coordinates of a moving target like a human may prevent reaching the required level of accuracy. This paper proposes a novel solution that may overcome this issue. It foresees calculating the relative altitude of the drone from the target by knowing only the ground distance between two points visible in the image. This relative altitude can be then used to calculate the target-to-camera distance without using the coordinates of the drone or the target. The procedure was verified with real data collected with a quadcopter, first considering a controlled environment with a wooden pole of known height and then a person in a more realistic scenario. The verification confirmed that a high level of accuracy can be reached, even with regular market drones. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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24 pages, 8711 KiB  
Article
An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud
by Bahram Salehi, Sina Jarahizadeh and Amin Sarafraz
Remote Sens. 2022, 14(19), 4917; https://doi.org/10.3390/rs14194917 - 01 Oct 2022
Cited by 7 | Viewed by 2186
Abstract
A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, [...] Read more.
A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV): New Solutions and Applications for Real-Life Tasks)
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