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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 26–50 of 127 papers

TitleStatusHype
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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