The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Vehicle Detection in UAV Traffic Video Based on Convolution Neural Network
Abstract
Vehicle detection technology is a key component of an intelligent transportation system, but most of the current vehicle detection technologies are based on road monitoring cameras. Compared with these fixed cameras, Unmanned Aerial Vehicles (UAVs) seem to have a lot of advantages such as more flexible, broader vision, higher speed, which make the vehicle detection more challenging. In this paper, a new dataset built on UAV traffic videos and a neural network which could fuse multi-layer features are proposed. Different from some networks with only a single layer, the proposed network merges the features from multiple layers firstly. Then a convolution layer is used to reduce the feature dimensions and a deconvolution layer is employed to do upsampling and enhance the response information. Finally, multiple fully connected layers are used to finish the detection. Furthermore, the proposed method combines the detecting and tracking for optimization and high detection speed. Experiments on the self-built UAV traffic video dataset demonstrate that the proposed method gets better results and higher speed.
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Combining different datasets does not guarantee an improvement in vehicle detection performance.
“Also, combining different datasets does not guarantee an improvement in performance.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [98]
[98] suggested that SSD is promising for vehicle detection in aerial imagery and its inherent multi-scale feature structure facilitated multi-size object detection, making it suitable for traffic monitoring at generically high altitudes.
“In this connection, [98] suggested that the SSD framework is promising for vehicle detection task in aerial imagery and its inherent multi-scale feature structure facilitated multisize object detection, making this solution suitable for traffic monitoring at generically high altitudes.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [98]
The Faster R-CNN, R-FCN & SSD with ResNet-50 achieves an F1 score of 86.10% for single class on a custom dataset.
“[98] | Faster R-CNN, R-FCN & SSD | ResNet-50 | Custom | 3840 × 2160 (25fps) | -- | Top view | i. Single class ii. Multi-class (Ordinary & Heavy vehicles) | -- | i. 86.10 ii. 89.30 | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [98]
The Faster R-CNN, R-FCN & SSD with ResNet-50 achieves an F1 score of 89.30% for multi-class on a custom dataset.
“[98] | Faster R-CNN, R-FCN & SSD | ResNet-50 | Custom | 3840 × 2160 (25fps) | -- | Top view | i. Single class ii. Multi-class (Ordinary & Heavy vehicles) | -- | i. 86.10 ii. 89.30 | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [98]
Citations
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Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
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Flagged first, then by citations
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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
Metadata
- Authors
- Li, S., Zhang, W., Li, G., Su, L., & Huang, Q.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Chinese Academy of Sciences (CN)
- Venue
- 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 1-6
- OpenAlex
- W2811123461
- Cited by
- 12
- References
- 5
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 1
- FWCI
- 0.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) (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, unmanned aerial vehicles, multi-layer feature fusion, intelligent transportation systems, real-time detection
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