Traffic Sign Recognition
38 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
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.
NetTailor: Tuning the Architecture, Not Just the Weights
pedro-morgado/nettailor
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CVPR 2019
Under the standard paradigm of network fine-tuning, an entirely new CNN is learned per task, and the final network size is independent of task complexity.
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
UMBCvision/Universal-Litmus-Patterns
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CVPR 2020
In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.
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.
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image.
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide.
Metric Learning for Novelty and Anomaly Detection
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
TrustAI/DeepGame
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In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations.
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework.
