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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 )

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Datasets

Greatest papers with code

Multi-column Deep Neural Networks for Image Classification

13 Feb 2012hughperkins/DeepCL

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs.

GENERAL CLASSIFICATION IMAGE CLASSIFICATION TRAFFIC SIGN RECOGNITION

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

20 Jun 2018bosch-ros-pkg/bstld

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.

TRAFFIC SIGN DETECTION TRAFFIC SIGN RECOGNITION

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

11 May 2018dineshresearch/Novel-Deep-Learning-Model-for-Traffic-Sign-Detection-Using-Capsule-Networks

Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer. This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset. Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation by using the dynamic routing and route by agreement algorithms. unlike the previous approaches of manual feature extraction, multiple deep neural networks with many parameters, our method eliminates the manual effort and provides resistance to the spatial variances. CNNs can be fooled easily using various adversary attacks and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles. Capsule network have achieved the state-of-the-art accuracy of 97. 6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).

TRAFFIC SIGN DETECTION TRAFFIC SIGN RECOGNITION

Traffic Sign Classification Using Deep Inception Based Convolutional Networks

10 Nov 2015vxy10/p2-TrafficSigns

In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods.

GENERAL CLASSIFICATION TRAFFIC SIGN RECOGNITION

NetTailor: Tuning the Architecture, Not Just the Weights

CVPR 2019 pedro-morgado/nettailor

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.

CONTINUAL LEARNING OBJECT RECOGNITION TRAFFIC SIGN RECOGNITION TRANSFER LEARNING

Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

29 Aug 2019olivesgatech/CURE-TSD

We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.

TRAFFIC SIGN DETECTION TRAFFIC SIGN RECOGNITION

Metric Learning for Novelty and Anomaly Detection

16 Aug 2018mmasana/OoD_Mining

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.

ANOMALY DETECTION METRIC LEARNING OUT-OF-DISTRIBUTION DETECTION TRAFFIC SIGN RECOGNITION

Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

23 Jun 2020JingWang18/Discriminative-Feature-Alignment

To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution.

DATA AUGMENTATION DOMAIN GENERALIZATION TRAFFIC SIGN RECOGNITION TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

CVPR 2020 UMBCvision/Universal-Litmus-Patterns

In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).

TRAFFIC SIGN RECOGNITION