The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Vehicle Detection Based on Drone Images with the Improved Faster R-CNN
Abstract
With the increasing number of vehicles, traffic management has put forward higher requirements for vehicle monitoring, thus the technology of vehicle detection based on drone images has received increasing attention. Firstly, we construct a new vehicle detection data set of 600 drone images so that to solve the vehicle detection tasks in real world. Secondly, aiming at the problem of false detection and missed detection in vehicle detection, the Faster R-CNN is improved by using ResNet and constructing Feature Pyramid Networks (FPN) to extract the image features. Finally, based on the vehicle detection data set, the improved Faster R-CNN can be used to detect vehicle targets. The experiment results show that the accuracy of improved method is 96.83%, which is 3.86% higher than that of the original Faster R-CNN method.
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In the citing paper
“The Butterfly detector is an anchor-free detector that overcomes the disadvantages of both anchor-based and anchor-free detectors by introducing the characteristics such as locating the center, width, and height information of the object of interest and generating the butterfly field from objectspecific features with specific aspect ratios.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [74]
In the citing paper
“To analyze the traffic from UAV images, [74] designed a Butterfly detector to handle the difficulties of wide range of object scale, viewing angle fluctuation, and occlusion.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [74]
In the citing paper
“This is achieved by introducing the butterfly field concept, which describes the spatial information of output characteristics and object scale, while occlusion and viewing angle difficulties are addressed by using a voting system between butterfly vectors pointing to the object's center.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [74]
In the citing paper
“[74] Butterfly | | HRNet | i. VisDrone2019 ii. UAVDT | 2000 × 1500 1080 × 540 | Different altitudes | Different Viewing angles | VisDrone:10 Classes UAVDT: 3 Classes | i. 30.15 ii. 58.1 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [74]
In the citing paper
“[74] Butterfly | | HRNet | i. VisDrone2019 ii. UAVDT | 2000 × 1500 1080 × 540 | Different altitudes | Different Viewing angles | VisDrone:10 Classes UAVDT: 3 Classes | i. 30.15 ii. 58.1 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [74]
Citations
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You Only Look Once: Unified, Real-Time Object Detection
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Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
Mundhenk, T. N., Konjevod, G., Sakla, W. A., & Boakye, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_48 · Cited by 386
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Cited by
Sorted by citations
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 139
“Moreover, as per [74], lack of depth features leads targets to be missed, while impediments such as shadows and trees induce false object detection.”
BackgroundMethodInfluential
Metadata
- Authors
- Wang, L., Liao, J., & Xu, C.
- Affiliations
- Hunan University of Science and Technology (CN)
- Venue
- Proceedings of the 2019 11th International Conference on Machine Learning and Computing
- Publisher
- ACM
- Published
- 2019-02-22
- Pages
- 466-471
- OpenAlex
- W2945659937
- Cited by
- 31
- References
- 4
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 5 · 2019: 1
- FWCI
- 1.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Computing (ICMLC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- vehicle detection, drone images, Faster R-CNN, ResNet, feature pyramid network, false detection, missed detection, traffic monitoring
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- Wang, L., Liao, J., & Xu, C. (2019). Vehicle Detection Based on Drone Images with the Improved Faster R-CNN. Proceedings of the 2019 11th International Conference on Machine Learning and Computing. https://doi.org/10.1145/3318299.3318383
