Traffic Sign Detection
14 papers with code • 3 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Traffic Sign Detection
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CURE-TSR
Latest papers
Learning to Defer to a Population: A Meta-Learning Approach
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert.
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
Traffic sign detection is an important research direction in intelligent driving.
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
It provides an essential enrichment to the widely used LISA Traffic Sign dataset.
Improved YOLOv5 network for real-time multi-scale traffic sign detection
NWPU-Li/AF_FPN
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Moreover, in practical application, it is difficult for common methods to improve the detection accuracy of multi-scale traffic signs while ensuring real-time detection.
A real-time and high-precision method for small traffic-signs recognition
However, in real applications, small traffic-signs recognition is still challenging.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
Model Agnostic Defence against Backdoor Attacks in Machine Learning
In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models.
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
LCAD-UFES/publications-tabelini-ijcnn-2019
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Deep learning has been successfully applied to several problems related to autonomous driving.
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
skokec/detectron-traffic-signs
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Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0. 17 in terms of precision and a performance drop of 0. 28 in recall under severe conditions.
