Traffic Sign Recognition

20 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 )

Latest papers without code

Differentiable Patch Selection for Image Recognition

7 Apr 2021

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand.

Traffic Sign Recognition

Online Defense of Trojaned Models using Misattributions

29 Mar 2021

This paper proposes a new approach to detecting neural Trojans on Deep Neural Networks during inference.

Traffic Sign Recognition

Sill-Net: Feature Augmentation with Separated Illumination Representation

6 Feb 2021

For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.

Few-Shot Image Classification Object Classification +2

Black-box Adversarial Attacks in Autonomous Vehicle Technology

15 Jan 2021

Also, the issue of late convergence in a Simple black-box attack (SimBA) is addressed by minimizing the loss of the most confused class which is the incorrect class predicted by the model with the highest probability, instead of trying to maximize the loss of the correct class.

Traffic Sign Recognition

Road images augmentation with synthetic traffic signs using neural networks

13 Jan 2021

Such training data is obtained by embedding synthetic images of signs in the real photos.

Traffic Sign Detection Traffic Sign Recognition

Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks

1 Dec 2020

Second, we find that compositional deep networks, which have part-based representations that lead to innate robustness to natural occlusion, are robust to patch attacks on PASCAL3D+ and the German Traffic Sign Recognition Benchmark, without adversarial training.

Traffic Sign Recognition

Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture

13 Oct 2020

In this paper, an efficient solution to enhance road signs detection, including Arabic context, performance based on color segmentation, Randomized Hough Transform and the combination of Zernike moments and Haralick features has been made.

Traffic Sign Detection Traffic Sign Recognition

Visualizing Color-wise Saliency of Black-Box Image Classification Models

6 Oct 2020

Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, which explains the significance of each pixel.

Classification General Classification +2

Learning From Context-Agnostic Synthetic Data

29 May 2020

We present a new approach for synthesizing training data given only a single example of each class.

Few-Shot Learning Image Classification +1

Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks

29 May 2020

We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset.

Classification General Classification +1