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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 101–127 of 127 papers

TitleStatusHype
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks—0
Black-box Adversarial Attacks in Autonomous Vehicle Technology—0
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks—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
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix—0
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors—0
Differentiable Patch Selection for Image Recognition—0
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map—0
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model—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
Evil from Within: Machine Learning Backdoors through Hardware Trojans—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
Fooling a Real Car with Adversarial Traffic Signs—0
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Improving Prediction Confidence in Learning-Enabled Autonomous Systems—0
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