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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 51–100 of 127 papers

TitleStatusHype
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception—0
Why Autonomous Vehicles Are Not Ready Yet: A Multi-Disciplinary Review of Problems, Attempted Solutions, and Future Directions—0
Traffic Sign Recognition Using Local Vision Transformer—0
Efficient Vision Transformer for Accurate Traffic Sign Detection—0
Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood—0
A Deeply Supervised Semantic Segmentation Method Based on GAN—0
Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade DefenseCode0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Adversarial Robustness Certification for Bayesian Neural NetworksCode0
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning—0
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
Traffic Sign Recognition Dataset and Data AugmentationCode0
Architecturing Binarized Neural Networks for Traffic Sign RecognitionCode0
Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study—0
Towards Audit Requirements for AI-based Systems in Mobility Applications—0
M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition—0
Traffic Sign Classification Using Deep and Quantum Neural Networks—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition—0
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation—0
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without SupervisionCode0
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems—0
Improving traffic sign recognition by active searchCode0
NTD: Non-Transferability Enabled Backdoor Detection—0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
Improving Prediction Confidence in Learning-Enabled Autonomous Systems—0
Robustness testing of AI systems: A case study for traffic sign recognition—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Differentiable Patch Selection for Image Recognition—0
MISA: Online Defense of Trojaned Models using Misattributions—0
Black-box Adversarial Attacks in Autonomous Vehicle Technology—0
Road images augmentation with synthetic traffic signs using neural networks—0
Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks—0
Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture—0
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign RecognitionCode0
Visualizing Color-wise Saliency of Black-Box Image Classification Models—0
Towards Context-Agnostic Learning Using Synthetic DataCode0
Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks—0
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix—0
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral CharacteristicsCode0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
Fooling a Real Car with Adversarial Traffic Signs—0
NetTailor: Tuning the Architecture, Not Just the WeightsCode0
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNsCode0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
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