Traffic Sign Recognition

20 papers with code • 5 benchmarks • 4 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 )

Latest papers with code

Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

AdvAttack/RoadSignAttack 9 Oct 2020

To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.

Traffic Sign Recognition

0
09 Oct 2020

SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

ssloxford/short-lived-adversarial-perturbations 8 Jul 2020

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.

Traffic Sign Recognition

5
08 Jul 2020

Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

JingWang18/Discriminative-Feature-Alignment 23 Jun 2020

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.

Data Augmentation Domain Generalization +3

32
23 Jun 2020

Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

olivesgatech/CURE-TSD 29 Aug 2019

We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.

Traffic Sign Detection Traffic Sign Recognition

33
29 Aug 2019

NetTailor: Tuning the Architecture, Not Just the Weights

pedro-morgado/nettailor CVPR 2019

Under the standard paradigm of network fine-tuning, an entirely new CNN is learned per task, and the final network size is independent of task complexity.

Continual Learning Object Recognition +2

43
29 Jun 2019

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

UMBCvision/Universal-Litmus-Patterns CVPR 2020

In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).

Traffic Sign Recognition

24
26 Jun 2019

Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

Shawn-Shan/trapdoor 18 Apr 2019

Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.

Adversarial Attack Detection Adversarial Defense +4

15
18 Apr 2019

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

skokec/detectron-traffic-signs 1 Apr 2019

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.

Traffic Sign Detection Traffic Sign Recognition

15
01 Apr 2019

Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Sourajit2110/DilatedSkipTotalRecall 30 Aug 2018

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide.

Self-Driving Cars Traffic Sign Recognition

7
30 Aug 2018

Metric Learning for Novelty and Anomaly Detection

mmasana/OoD_Mining 16 Aug 2018

When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.

Anomaly Detection Metric Learning +2

31
16 Aug 2018