Advancing Automatic Asset Management: An Edge-Based US-Specific Traffic Sign Detection and Recognition System Based on Image Processing

Abstract

Effective asset management is crucial in transportation systems, guaranteeing the optimal use, upkeep, and durability of infrastructure, which in turn boosts the safety, efficiency, and sustainability of travel networks. Traffic signs management, as a vital component of asset management, plays a key role in maintaining road safety and efficiency; however, their maintenance demands substantial time and resources investment. The advent of advanced sensing technologies in intelligent transportation systems (ITS) presents an opportunity for more effective and precise asset management. Yet, the challenge lies in the lack of localized traffic sign data in the US, which hinders the implementation of these technologies. To address this gap, our research introduces a traffic sign detection and recognition (TSDR) architecture designed to automatically collect traffic sign information and establish a US-specific data inventory. Recognizing the limitations of existing public traffic sign data sets, which are not tailored for US traffic signs, we collected an additional 5,000 traffic sign images from the Washington State area using Google Map application programming interface (API) and self-installed dash cameras. These signs were manually labeled into 43 classes for training purposes. With the training process, the proposed TSDR model can achieve impressive accuracy (98.34% in detection and 97.10% in recognition). In summary, we developed an automated pipeline, TSDR, for capturing, detecting, classifying, and storing traffic signs, culminating in the creation of a localized traffic sign data inventory for the US.