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Object Recognition in Aerial Images Using Convolutional Neural Networks
Open accessAbstract
There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.
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“Concerning the real-time detection and tracking of vehicles in aerial videos, [96] showed that YOLOv2 framework [83] provides higher accuracy and faster processing compared to YOLO [108] and SSD [82] frameworks.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [96]
In the citing paper
“[96] YOLOv2 | | CNN | i. Custom ii. Stanford drone | -- | Different altitudes | Both static & moving UAV states | Four classes (Bicyclist, Skateboarder, Car, Bus) | i. 77.12 ii. 68 | -- | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [96]
In the citing paper
“[96] YOLOv2 | | CNN | i. Custom ii. Stanford drone | -- | Different altitudes | Both static & moving UAV states | Four classes (Bicyclist, Skateboarder, Car, Bus) | i. 77.12 ii. 68 | -- | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [96]
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References 14 · Cited by 201
Research works printed but not linked: 11
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Metadata
- Authors
- Radovic, M., Adarkwa, O., & Wang, Q.
- Affiliations
- University of Delaware (US)
- Iowa State University (US)
- Corresponding author
- Radovic, Matija
- ORCID
- Wang, Qiaosong
- Venue
- Journal of Imaging
- Publisher
- MDPI AG
- Published
- 2017-06-14
- Volume
- 3
- Issue
- 2
- Pages
- 21
- ISSN
- 2313-433X (ISSN-L)
- OpenAlex
- W2624848721
- Cited by
- 201
- References
- 14
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 25 · 2023: 25 · 2022: 24 · 2021: 30 · 2020: 32 · 2019: 26 · 2018: 18 · 2017: 1
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Imaging (publishedVersion, cc-by)
- SSRN Electronic Journal (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object recognition, aerial images, unmanned aerial vehicles, convolutional neural networks, YOLO, object detection, object classification, real-time video processing, infrastructure asset management, bridge inspection, transportation applications
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