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
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.
A real-time and high-precision method for small traffic-signs recognition
However, in real applications, small traffic-signs recognition is still challenging.
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.
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.
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.
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.
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.
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.
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.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
