HybridNet: A fast vehicle detection system for autonomous driving
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos, and their speed and simplicity make them well-suited for real-time detection scenarios.
“Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos. Their speed and simplicity make them particularly well-suited for scenarios where real-time detection is crucial. These applications are supported by numerous studies that highlight the efficiency and effectiveness of single-stage detec”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [26]
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 56,035 · 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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
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 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · see citing works
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801 · see citing works
SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection
Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., & Heng, P.-A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2838132 · Cited by 341 · see citing works
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.384 · Cited by 326 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
Improved YOLOv4 approach: A real time occluded vehicle detection
Kumar, S., Jailia, M., & Varshney, S. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/120139 · Cited by 7 · see citing works
- Authors
- Dai, X.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Dai, Xuerui
- ORCID
- Dai, Xuerui
- Venue
- Signal Processing: Image Communication
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 70
- Pages
- 79-88
- ISSN
- 0923-5965 (ISSN-L)
- OpenAlex
- W2893538180
- Cited by
- 113
- References
- 11
- Citations by year
- 2026: 3 · 2025: 18 · 2024: 17 · 2023: 15 · 2022: 17 · 2021: 21 · 2020: 16 · 2019: 6
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Signal Processing Image Communication (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- vehicle detection, autonomous driving, two-stage object detection, real-time detection, KITTI dataset, Pascal VOC 2007
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
