Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

121

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

121

“…A study comparing various visual descriptors is present in Russell and Fischaber [25] while Ding et al [26] develop a system for detection and identification using the SURF (speeded-up robust features) [27] algorithm and GPGPU (general-purpose gpu) programming model. In Lin et al [28], there is a hybrid approach based on adaptive image pre-processing models and two fuzzy inference schemes checking light illumination and the red color amount of a frame image.…”

Section: Related Workmentioning

confidence: 99%

Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods

Balducci

Impedovo

Pirlo

2018

This work presents the practical design of a system that faces the problem of identification and validation of private no-parking road signs. This issue is very important for the public city administrations since many people, after receiving a code that identifies the signal at the entrance of their private car garage as valid, forget to renew the code validity through the payment of a city tax, causing large money shortages to the public administration. The goal of the system is twice since, after recognition of the official road sign pattern, its validity must be controlled by extracting the code put in a specific sub-region inside it. Despite a lot of work on the road signs’ topic having been carried out, a complete benchmark dataset also considering the particular setting of the Italian law is today not available for comparison, thus the second goal of this work is to provide experimental results that exploit machine learning and deep learning techniques that can be satisfactorily used in industrial applications.

“…All researchers are implementing their methods to achieve a common goal [ 25 ]. Some researchers have done the detection [ 26 , 27 , 28 ] part, some are tracking the detection and a few have described effective recognition parts [ 29 , 30 ]. According to Paclík et al [ 31 ], an automated traffic sign detection system was introduced for the first time in Japan in 1984.…”

Section: Related Workmentioning

confidence: 99%

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Islam

Raj

2017

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.