Citation

Balducci F, Impedovo D, Pirlo G. Sensors (Basel) 2018; 18(12): s18124147.

Affiliation

Dipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125 Bari, Italy. giuseppe.pirlo@uniba.it.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18124147

PMID

30486317

Abstract

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.

Language: en

Keywords

Italian road sign; Tow-away sign; deep learning; pattern recognition