Fast Deep Vehicle Detection in Aerial Images
Open accessAbstract
Vehicle detection in aerial images is a crucial image processing step for many applications like screening of large areas. In recent years, several deep learning based frameworks have been proposed for object detection. However, these detectors were developed for datasets that considerably differ from aerial images. In this paper, we systematically investigate the potential of Fast R-CNN and Faster R-CNN for aerial images, which achieve top performing results on common detection benchmark datasets. Therefore, the applicability of 8 state-of-the-art object proposals methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations of the object proposals methods are provided. To overcome shortcomings of the original approach in case of handling small instances, we further propose our own network that clearly outperforms state-of-the-art methods for vehicle detection in aerial images. All experiments are performed on two publicly available datasets to account for differing characteristics such as ground sampling distance, number of objects per image and varying backgrounds.
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DYOLO outperformed Faster R-CNN, SSD, and YOLOv2 frameworks.
“As a consequence, DY-OLO outperformed Faster Region-based CNN (Faster R-CNN) [84], SSD [82], and YOLOv2 [83] frameworks.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
In [75], with Faster R-CNN, low-level features were coupled with high-level features utilizing FPN to solve these difficulties.
“Therefore, in [75], with the Faster R-CNN framework [84], low-level features were coupled with high-level features utilizing Feature Pyramid Networks (FPN) to solve these difficulties.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
Results of ISPDM increased over base YOLOv3, Faster R-CNN, and SSD frameworks.
“Results increased over base YOLOv3 [25], Faster R-CNN [84] and SSD [82] frameworks.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
The Butterfly detector outperformed Faster R-CNN, CenterNet, and ClusDet frameworks while improved performance than YOLOv3, SSD, and GANet frameworks for UAVDT dataset.
“The results demonstrated that this framework outperformed Faster R-CNN [84], CenterNet [85], and ClusDet [86] frameworks while improved performance than YOLOv3 [25], SSD [82], and Guided Attention Network (GANet) [87] frameworks for UAVDT dataset [45].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
[73] used Faster R-CNN and YOLOv3 to detect cars for traffic monitoring purpose.
“Moreover, [73] used Faster R-CNN [84] and YOLOv3 [25] to detect cars for traffic monitoring purpose.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
[76] proposed Faster R-CNN framework with domain adaptation concept and using GANs, specifically cycle GAN approach, to turn annotated daytime images into unlabelled nighttime images.
“In this context, [76] proposed Faster R-CNN framework [84] with domain adaptation concept and using Generative Adversarial Networks (GANs), specifically cycle GAN approach [91], annotated daytime images successfully turned into unlabelled nighttime images.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
Experimental tests showed SSD has good performance compared to Faster R-CNN and R-FCN detectors.
“Experimental tests showed that the SSD framework [82] has good performance compared to Faster R-CNN [84] and Region-based Fully Convolutional Network (R-FCN) [111] detectors.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [84]
Vehicles can be detected in high-resolution satellite images with high accuracy, and image counts can estimate average vehicle traffic.
“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
From Tackling climate change with machine learning (2023) · cites this paper as [747]
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Semantic labeling for improved vehicle detection in aerial imagery
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- Authors
- Sommer, L. W., Schuchert, T., & Beyerer, J.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (DE)
- Fraunhofer IOSB, Karlsruhe, Germany
- ORCID
- Sommer, Lars Wilko
- Venue
- 2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 311-319
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2612465689
- Cited by
- 139
- References
- 27
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 12 · 2023: 12 · 2022: 16 · 2021: 23 · 2020: 21 · 2019: 19 · 2018: 14 · 2017: 6
- FWCI
- 4.80
- Type
- conference paper
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- vehicle detection, aerial images, Fast R-CNN, Faster R-CNN, small object detection, aerial object detection, ground sampling distance
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