Traffic Sign Recognition
37 papers with code • 10 benchmarks • 7 datasets
Traffic sign recognition is the task of recognising traffic signs in an image or video.
( Image credit: Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks )
Benchmarks
These leaderboards are used to track progress in Traffic Sign Recognition
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Libraries
Use these libraries to find Traffic Sign Recognition models and implementations
GTSRB
FlickrLogos-32
CURE-TSR
Tsinghua-Tencent 100K
BelgaLogos
Latest papers
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition
The results of the 4th International Verification of Neural Networks Competition (VNN-COMP'23) revealed the fact that 4, out of 7, solvers can handle many of our benchmarks randomly selected (minimum is 6, maximum is 36, out of 45).
Adversarial Robustness Certification for Bayesian Neural Networks
matthewwicker/adversarialrobustnesscertificationforbnns
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We study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations.
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping.
Architecturing Binarized Neural Networks for Traffic Sign Recognition
shyhyawJou/Binarized-Neural-Network
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Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving.
Robust Transformer with Locality Inductive Bias and Feature Normalization
Omid-Nejati/Locality-iN-Locality
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In this paper, we explore the robustness of vision transformers against adversarial perturbations and try to enhance their robustness/accuracy trade-off in white box attack settings.
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency.
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
It provides an essential enrichment to the widely used LISA Traffic Sign dataset.
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold.
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
hncszyq/ShadowAttack
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CVPR 2022
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
