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Traffic Sign Recognition

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 )

Papers

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Showing 1–50 of 127 papers

TitleStatusHype
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning MonitoringCode1
Adversarial Sticker: A Stealthy Attack Method in the Physical WorldCode1
A real-time and high-precision method for small traffic-signs recognitionCode1
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial DefenseCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch AttacksCode1
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingCode1
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural PhenomenonCode1
GLARE: A Dataset for Traffic Sign Detection in Sun GlareCode1
Robust Transformer with Locality Inductive Bias and Feature NormalizationCode1
SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial PerturbationsCode1
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign RecognitionCode1
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition SystemsCode1
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning—0
Adversarial Robustness Through Artifact Design—0
Improving Prediction Confidence in Learning-Enabled Autonomous Systems—0
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets—0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle—0
Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
Black-box Adversarial Attacks in Autonomous Vehicle Technology—0
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition—0
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition—0
A novel pLSA based Traffic Signs Classification System—0
Why Autonomous Vehicles Are Not Ready Yet: A Multi-Disciplinary Review of Problems, Attempted Solutions, and Future Directions—0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System—0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava—0
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model—0
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map—0
Differentiable Patch Selection for Image Recognition—0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition—0
Efficient Traffic-Sign Recognition with Scale-aware CNN—0
Efficient Vision Transformer for Accurate Traffic Sign Detection—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks—0
A Deeply Supervised Semantic Segmentation Method Based on GAN—0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning—0
Feature-Guided Black-Box Safety Testing of Deep Neural Networks—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
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