Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on multiscale performance. Our integrates dilated spatial pyramid pooling model (DSPP) and feature residual aggregation module (FRAM) to aggregate features at various...

There has been an increasing concern about inactive drivers who would easily lead to road accidents and fatalities once return to driving. This study investigated the re-usability of traffic signs for inactive drivers with consideration of driver factors and cognitive sign features. Fifty-seven Hong Kong Chinese, who possessed a full driving license but had not driven for an extended period, co...

This paper purposed a Traffic Sign Recognition (TSR) system which can automatically detect and classified the traffic signs in traffic scene images acquired from a moving car. First it uses color based segmentation and then further refines the segments using two shape detection based techniques. A color description technique is used to extract the sign information from the segmented part which ...

A fast method for the recognition and classification of informational traffic signs is presented in this paper. The aim is to provide an efficient framework which could be easily used in inventory and guidance systems. The process consists of several steps which include image segmentation, sign detection and reorientation, and finally traffic sign recognition. In a first stage, a static HSI col...

This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method uses the RGB-normalized (ErEgEb) color space for ROIs (Regions of Interest) generation based on a chr...

Abstract—To ensure a smooth and secure flow of traffic, road signs are essential. A major cause accidents is negligence in viewing the Traffic signboards interpreting them incorrectly. The proposed system helps recognizing sign sending voice alert through speaker to driver so that he/ she may take necessary decisions. trained using Convolutional Neural Network (CNN) which traffic image recognit...

A biologically plausible model of traffic sign detection and recognition invariantly with respect to variable viewing conditions is presented. The model simulates several key mechanisms of biological vision, such as space-variant representation of information (reduction in resolution from the fovea to retinal periphery), orientation selectivity in the cortical neuron responses, and context enco...

A traffic sign classification method based on artificial neural network is proposed in this paper. The proposed method for classifying traffic signs first detects traffic signs by using on the property of color probability model and then classifies the detected traffic signals. In both of detection and classification processes, two artificial neural network models are utilized. Experiments on p...

The purpose of this research is development of an algorithm for hardware implementation for number recognition applying in speed traffic-sign recognition system for car driving assistant. We recognize the speed limit of the speed traffic-sign using hardware oriented extraction algorithm. The numbers are recognized by comparing their feature values with the recognized features. The proposed hard...

We study the problem of traffic sign detection in the context of traffic infrastructure inventory. The data acquired during filming the roads in Croatia is presented. Based on recent approaches, and motivated by constraints present in our data, we employ the Viola-Jones object detector for triangular warning signs detection. The detector achieves correct detection rates better than 90%, which i...