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Traffic Sign Recognition
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Traffic sign recognition is the task of recognising traffic signs in an image or video.
<span style="color:grey; opacity: 0.6">( Image credit: Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving
Securing Traffic Sign Recognition Systems in Autonomous Vehicles
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection
Snowball Adversarial Attack on Traffic Sign Classification
Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder
Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulator
On the Relation between Optical Aperture and Automotive Object Detection
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines
LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks
AppSign: Multi-level Approximate Computing for Real-Time Traffic Sign Recognition in Autonomous Vehicles
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava
A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System
FUSED-Net: Detecting Traffic Signs with Limited Data
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective
Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks
Think Twice Before Recognizing: Large Multimodal Models for General Fine-grained Traffic Sign Recognition
Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity
Revolutionizing Traffic Sign Recognition: Unveiling the Potential of Vision Transformers
Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network
Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle
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