UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking
Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/17/12/594 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846106283192614912 |
|---|---|
| author | Md. Mahfuzur Rahman Sunzida Siddique Marufa Kamal Rakib Hossain Rifat Kishor Datta Gupta |
| author_facet | Md. Mahfuzur Rahman Sunzida Siddique Marufa Kamal Rakib Hossain Rifat Kishor Datta Gupta |
| author_sort | Md. Mahfuzur Rahman |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. |
| format | Article |
| id | doaj-art-891c7e46d4e64cdfb5e2bd019f2c580a |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-891c7e46d4e64cdfb5e2bd019f2c580a2024-12-27T14:05:21ZengMDPI AGAlgorithms1999-48932024-12-01171259410.3390/a17120594UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and TrackingMd. Mahfuzur Rahman0Sunzida Siddique1Marufa Kamal2Rakib Hossain Rifat3Kishor Datta Gupta4Silicon Orchard Research and Analytics Lab, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, BRAC University, Dhaka 1212, BangladeshDepartment of Computer Science, Texas Tech University, Lubbock, TX 79409, USADepartment of Cyber Physical Systems, Clark Atlanta University, Atlanta, GA 30314, USAUnmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.https://www.mdpi.com/1999-4893/17/12/594UAV (unmanned aerial vehicle)UAV datasetsobject detectionsemantic segmentationaction recognitionevent recognition |
| spellingShingle | Md. Mahfuzur Rahman Sunzida Siddique Marufa Kamal Rakib Hossain Rifat Kishor Datta Gupta UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking Algorithms UAV (unmanned aerial vehicle) UAV datasets object detection semantic segmentation action recognition event recognition |
| title | UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking |
| title_full | UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking |
| title_fullStr | UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking |
| title_full_unstemmed | UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking |
| title_short | UAV (Unmanned Aerial Vehicle): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking |
| title_sort | uav unmanned aerial vehicle diverse applications of uav datasets in segmentation classification detection and tracking |
| topic | UAV (unmanned aerial vehicle) UAV datasets object detection semantic segmentation action recognition event recognition |
| url | https://www.mdpi.com/1999-4893/17/12/594 |
| work_keys_str_mv | AT mdmahfuzurrahman uavunmannedaerialvehiclediverseapplicationsofuavdatasetsinsegmentationclassificationdetectionandtracking AT sunzidasiddique uavunmannedaerialvehiclediverseapplicationsofuavdatasetsinsegmentationclassificationdetectionandtracking AT marufakamal uavunmannedaerialvehiclediverseapplicationsofuavdatasetsinsegmentationclassificationdetectionandtracking AT rakibhossainrifat uavunmannedaerialvehiclediverseapplicationsofuavdatasetsinsegmentationclassificationdetectionandtracking AT kishordattagupta uavunmannedaerialvehiclediverseapplicationsofuavdatasetsinsegmentationclassificationdetectionandtracking |