The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Fast vehicle detection in UAV images
Abstract
Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) images remains a challenge, due to its very high spatial resolution and very few annotations. Although numerous vehicle detection methods exist, most of them cannot achieve real-time detection for different scenes. Recently, deep learning algorithms has achieved fantastic detection performance in computer vision, especially regression based convolutional neural networks YOLOv2. It's good both at accuracy and speed, outperforming other state-of-the-art detection methods. This paper for the first time aims to investigate the use of YOLOv2 for vehicle detection in UAV images, as well as to explore the new method for data annotation. Our method starts with image annotation and data augmentation. CSK tracking method is used to help annotate vehicles in images captured from simple scenes. Subsequently, a regression based single convolutional neural network YOLOv2 is used to detect vehicles in UAV images. To evaluate our method, UAV video images were taken over several urban areas, and experiments were conducted on this dataset and Stanford Drone dataset. The experimental results have proven that our data preparation strategy is useful, and YOLOv2 is effective for real-time vehicle detection of UAV video images.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In order to address this issue, [95] trained Deeply Supervised Object Detector (DSOD) learning model from scratch, entirely on UAV dataset rather than using a pre-trained model, showing performance improvement, both in terms of precision and recall.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [95]
In the citing paper
“Therefore, the use of weights trained using drone-based large scale imagery would produce a performance boost [95].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [95]
In the citing paper
“When compared to KF-based tracking, LSTM shows improved tracking performance.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [95]
Citations
Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,272
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Research works printed but not linked: 3
- 10.1109/lgrs.2014.2309695 · DOI
- joseph, 2016, YOL09000 Better Faster Stronger
- 0, Computational vision and Geometry lab Stanford Drone Dataset
Cited by
Deep learning-based object detection in low-altitude UAV datasets: A survey
Mittal, P., Singh, R., & Sharma, A. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104046 · Cited by 356
UAV in the advent of the twenties: Where we stand and what is next
Nex, F., Armenakis, C., Cramer, M., Cucci, D. A., Gerke, M., Honkavaara, E., Kukko, A., Persello, C., & Škaloud, J. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.12.006 · Cited by 352
Vehicle Detection From UAV Imagery With Deep Learning: A Review
Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3080276 · Cited by 202
Automated detection of wildlife using drones: Synthesis, opportunities and constraints
Corcoran, E. M., Anschau, M., Sudholz, A., & Hamilton, G. S. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13581 · Cited by 197
Bakırcı, M. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112015 · Cited by 160
Metadata
- Authors
- Tang, T., Deng, Z., Zhou, S., Lei, L., & Zou, H.
- Affiliations
- National University of Defense Technology (CN)
- Venue
- 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 1-5
- OpenAlex
- W2695028939
- Cited by
- 74
- References
- 27
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 15 · 2020: 11 · 2019: 10 · 2018: 7
- FWCI
- 2.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Workshop on Remote Sensing with Intelligent Processing (RSIP) (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, UAV imagery, YOLOv2, real-time object detection, data annotation, data augmentation, high-resolution aerial imagery, urban scenes
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
