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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 1–25 of 127 papers

TitleStatusHype
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving—0
Securing Traffic Sign Recognition Systems in Autonomous Vehicles—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles—0
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection—0
Snowball Adversarial Attack on Traffic Sign Classification—0
Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder—0
Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulator—0
On the Relation between Optical Aperture and Automotive Object Detection—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions—0
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard BaselinesCode0
LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks—0
AppSign: Multi-level Approximate Computing for Real-Time Traffic Sign Recognition in Autonomous Vehicles—0
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map—0
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets—0
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial DefenseCode1
YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion—0
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava—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
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign RecognitionCode0
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