Traffic Sign Recognition
38 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
Most implemented papers
Towards Context-Agnostic Learning Using Synthetic Data
charlesjin/synthetic_data
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NeurIPS 2021
We propose a novel setting for learning, where the input domain is the image of a map defined on the product of two sets, one of which completely determines the labels.
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
JingWang18/Discriminative-Feature-Alignment
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To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution.
SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations
ssloxford/short-lived-adversarial-perturbations
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Research into adversarial examples (AE) has developed rapidly, yet static adversarial patches are still the main technique for conducting attacks in the real world, despite being obvious, semi-permanent and unmodifiable once deployed.
Sill-Net: Feature Augmentation with Separated Illumination Representation
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.
Adversarial Sticker: A Stealthy Attack Method in the Physical World
jinyugy21/adv-stickers_rhde
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Unlike the previous adversarial patches by designing perturbations, our method manipulates the sticker's pasting position and rotation angle on the objects to perform physical attacks.
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems
The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly.
A real-time and high-precision method for small traffic-signs recognition
However, in real applications, small traffic-signs recognition is still challenging.
Improving traffic sign recognition by active search
samsja/finding-a-needle-code
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We demonstrate that by sorting the samples of a large, unlabeled set by the estimated probability of belonging to the rare class, we can efficiently identify samples from the rare class.
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.
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.
