Traffic Sign Recognition
37 papers with code • 10 benchmarks • 7 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Traffic Sign Recognition
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Libraries
Use these libraries to find Traffic Sign Recognition models and implementations
GTSRB
FlickrLogos-32
CURE-TSR
Tsinghua-Tencent 100K
BelgaLogos
Latest papers with no code
Adversarial Attacks on Traffic Sign Recognition: A Survey
In this work, we survey existing works performing either digital or real-world attacks on traffic sign detection and classification models.
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises.
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model
The widespread adoption of Image Processing has propelled Object Recognition (OR) models into essential roles across various applications, demonstrating the power of AI and enabling crucial services.
Evil from Within: Machine Learning Backdoors through Hardware Trojans
In this paper, we challenge this assumption and introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning.
Traffic Sign Recognition Dataset and Data Augmentation
Although there are many datasets for traffic sign classification, there are few datasets collected for traffic sign recognition and few of them obtain enough instances especially for training a model with the deep learning method.
Towards Audit Requirements for AI-based Systems in Mobility Applications
Various mobility applications like advanced driver assistance systems increasingly utilize artificial intelligence (AI) based functionalities.
Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study
In this work we apply different black-box attack methods to generate perturbations that are applied in the physical environment and can be used to fool systems under different environmental conditions.
M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition
Traffic sign recognition is committed to ensuring the safety of automatic driving.
Traffic Sign Classification Using Deep and Quantum Neural Networks
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision.
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents
The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc.
