Traffic Sign Recognition

26 papers with code • 5 benchmarks • 4 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 )


Use these libraries to find Traffic Sign Recognition models and implementations

Most implemented papers

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

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

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 Classification Using Deep Inception Based Convolutional Networks

vxy10/p2-TrafficSigns 10 Nov 2015

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

MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification

ppriyank/MicronNet 28 Mar 2018

The resulting MicronNet possesses a model size of just ~1MB and ~510, 000 parameters (~27x fewer parameters than state-of-the-art) while still achieving a human performance level top-1 accuracy of 98. 9% on the German traffic sign recognition benchmark.

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

olivesgatech/CURE-TSD 29 Aug 2019

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

Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition

AdvAttack/RoadSignAttack 9 Oct 2020

To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.

Multi-column Deep Neural Networks for Image Classification

hughperkins/DeepCL 13 Feb 2012

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

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

olivesgatech/CURE-TSR 7 Dec 2017

We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos

inspire-group/advml-traffic-sign 9 Jan 2018

Our attack pipeline generates adversarial samples which are robust to the environmental conditions and noisy image transformations present in the physical world.

DARTS: Deceiving Autonomous Cars with Toxic Signs

inspire-group/advml-traffic-sign 18 Feb 2018

In this paper, we propose and examine security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS).