Deep Neural Network Based Vehicle Detection and Classification of Aerial Images Article Swipe
Sandeep Kumar
,
Arpit Jain
,
Shilpa Rani
,
Hammam Alshazly
,
Sahar Ahmed Idris
,
Sami Bourouis
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.024812
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2022.024812
The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of existing techniques in this research work. We propose an algorithm based on Convolutional Neural Network (CNN) to detect the vehicle and classify it into light and heavy vehicles. The performance of this approach was evaluated using a variety of benchmark datasets, including VEDAI, VIVID, UC Merced Land Use, and the Self database. To validate the results, various performance parameters such as accuracy, precision, recall, error, and F1-Score were calculated. The results suggest that the proposed technique has a higher detection rate, which is approximately 92.06% on the VEDAI dataset, 95.73% on the VIVID dataset, 90.17% on the UC Merced Land dataset, and 96.16% on the Self dataset.
Related Topics
Concepts
Computer science
Convolutional neural network
Benchmark (surveying)
Artificial intelligence
Object detection
Field (mathematics)
Precision and recall
Pattern recognition (psychology)
Deep learning
Artificial neural network
Computer vision
Machine learning
Geography
Mathematics
Cartography
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2022.024812
- https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24812/TSP_IASC_24812.pdf
- OA Status
- hybrid
- Cited By
- 83
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226458968
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226458968Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/iasc.2022.024812Digital Object Identifier
- Title
-
Deep Neural Network Based Vehicle Detection and Classification of Aerial ImagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Sandeep Kumar, Arpit Jain, Shilpa Rani, Hammam Alshazly, Sahar Ahmed Idris, Sami BourouisList of authors in order
- Landing page
-
https://doi.org/10.32604/iasc.2022.024812Publisher landing page
- PDF URL
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https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24812/TSP_IASC_24812.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://file.techscience.com/ueditor/files/iasc/TSP_IASC-34-1/TSP_IASC_24812/TSP_IASC_24812.pdfDirect OA link when available
- Concepts
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Computer science, Convolutional neural network, Benchmark (surveying), Artificial intelligence, Object detection, Field (mathematics), Precision and recall, Pattern recognition (psychology), Deep learning, Artificial neural network, Computer vision, Machine learning, Geography, Mathematics, Cartography, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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83Total citation count in OpenAlex
- Citations by year (recent)
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2025: 22, 2024: 34, 2023: 18, 2022: 9Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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