Traffic-Sign Detection and Classification in the Wild
Abstract
Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. We call this benchmark Tsinghua-Tencent 100K. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify trafficsigns. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available1.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The self-driving system should perceive the driving environment, such as detecting road, traffic-sign, pedestrian, and car, estimating the intention of cars and pedestrians and predicting their trajectories, and measure the pose and location of landmarks for SLAM.
“The self-driving system should perceive the driving environment, such as detecting road [85], traffic-sign [259], pedestrian [260], and car [261], estimating the intention of cars and pedestrians and predicting their trajectories [244], [245]. Besides, it should also measure the pose and location of landmarks (e.g., trafficsigns) for SLAM [137].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [259]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
How good are detection proposals, really?
Hosang, J., Benenson, R., & Schiele, B. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.24 · Cited by 215 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
Recent advances in deep learning for object detection
Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047 · see citing works
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953 · see citing works
“[45] in terms of average recall and accuracy: 89% and 84% vs 87% and 82%, 96% and 91% vs 94% and 91%, 89% and 91% vs 88% and 91% on three subsets of different object sizes.”
BackgroundMethodInfluentialZhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465 · see citing works
Vision-based vehicle detection and counting system using deep learning in highway scenes
Song, H., Liang, H., Li, H., Dai, Z., & Yun, X. (2019) · European Transport Research Review · doi:10.1186/s12544-019-0390-4 · Cited by 392 · see citing works
Multimodal end-to-end autonomous driving
Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & Lopez, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3013234 · Cited by 319 · see citing works
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works
Performance vs. competence in human–machine comparisons
Firestone, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905334117 · Cited by 150 · see citing works
On Offline Evaluation of Vision-Based Driving Models
Codevilla, F., López, A. M., Koltun, V., & Dosovitskiy, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_15 · Cited by 103 · see citing works
Assessing Image Quality Issues for Real-World Problems
Chiu, T.-Y., Zhao, Y., & Gurari, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00370 · Cited by 63 · see citing works
- Authors
- Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S.
- Affiliations
- Tsinghua University (CN)
- Lehigh University (US)
- Tencent (China) (CN)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2110-2118
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2479866714
- Cited by
- 1,062
- References
- 5
- Citations by year
- 2026: 84 · 2025: 156 · 2024: 147 · 2023: 169 · 2022: 136 · 2021: 122 · 2020: 78 · 2019: 77 · 2018: 56 · 2017: 34 · 2016: 3
- FWCI
- 19.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Tencent
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- traffic sign detection, traffic sign classification, end-to-end convolutional neural network, small object detection
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
