Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
As per [75], lack of depth features leads targets to be missed, while impediments such as shadows and trees induce false object detection.
“Moreover, as per [75], lack of depth features leads targets to be missed, while impediments such as shadows and trees induce false object detection.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [75]
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 [75]
In [75], anchor sizes and ratios were optimized.
“Also, anchor sizes and ratios were optimized.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [75]
The Modified Faster R-CNN achieves an AP of 96.83% on a custom dataset.
“[75] | Modified Faster R-CNN | ResNet-101 | Custom | 9000 × 6700 | Fixed | Fixed | One class (Car) | 96.83 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [75]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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 · see citing works
AttGAN: Facial attribute editing by only changing what you want
He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2916751 · Cited by 833 · see citing works
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
Dong, Z., Wu, Y., Pei, M., & Jia, Y. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2402438 · Cited by 387 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3260040 · Cited by 161 · see citing works
Self-citationA 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 121 · see citing works
“Tracking based counting of vehicles is carried out in [3] and [66], where Zhu et al. [75] count 890 the detected vehicles in videos by estimating their motion.”
BackgroundMethodInfluential
- Authors
- Li, J., Xu, Z., Fu, L., Zhou, X., & Yu, H.
- Affiliations
- Cleveland State University (US)
- Chang'an University (CN)
- University of South Carolina (US)
- Arizona State University (US)
- Corresponding author
- Xu, Zhigang
- Yu, Hongkai
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 124
- Pages
- 102946
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W3122939190
- Cited by
- 80
- References
- 4
- Citations by year
- 2026: 2 · 2025: 15 · 2024: 15 · 2023: 23 · 2022: 18 · 2021: 7
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- EngagedScholarship @ Cleveland State University (Cleveland State University) (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- Key Research and Development Projects of Shaanxi Province
- National Key Research and Development Program of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- 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
- Keywords
- vehicle detection, domain adaptation, night imagery, traffic monitoring, Faster R-CNN, traffic flow theory
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Statistics and arithmetic are recomputed, not judged.
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