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
Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network
Traffic signs are important in communicating information to drivers.
Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks
In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation.
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle
In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle.
Adversarial Robustness Through Artifact Design
We evaluated our approach in the domain of traffic-sign recognition, allowing it to alter traffic-sign pictograms (i. e., symbols within the signs) and their colors.
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception
We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras.
Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions
Autonomous vehicle refers to a vehicle capable of perceiving its surrounding environment and driving with little or no human driver input.
Traffic Sign Recognition Using Local Vision Transformer
The experimental evaluations demonstrate that the hybrid network with the locality module outperforms pure transformer-based models and some of the best convolutional networks in accuracy.
Efficient Vision Transformer for Accurate Traffic Sign Detection
This research paper addresses the challenges associated with traffic sign detection in self-driving vehicles and driver assistance systems.
Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood
The project's primary objectives are to train the YOLOv5 model on a diverse dataset of traffic sign images and deploy the model on a suitable hardware platform capable of real-time inference.
A Deeply Supervised Semantic Segmentation Method Based on GAN
In recent years, the field of intelligent transportation has witnessed rapid advancements, driven by the increasing demand for automation and efficiency in transportation systems.
