Abstract
Traffic signs recognition (TSR) is a crucial sub-domain of computer vision, particularly relevant to autonomous vehicles and autonomous driver-assistance systems (ADAS). TSR systems can further augment efforts in many other applications, such as highway asset maintenance and management. Despite the relative success of detectors with hand-crafted features in Europe, most, if not all, non-deep-learning based systems are not scalable to accurately recognize a large subset of traffic signs in the United States. The recent works in the domain of object detection using machine learning have shown the necessity of deep neural networks (DNNs) in TSR, whereby a DNN can learn features automatically without hand-crafting them. Due to the lack of datasets in the U.S. and the inefficient use of traditional methods for traffic sign recognition (TSR) in the U.S., we created the Automotive Repository of Traffic Signs (ARTS), a new dataset for traffic signs recognition in the U.S. ARTS covers a wide range of sign-types, including Regulatory, Guide, Warning, and Temporary signs as defined in the Manual on Uniform Traffic Control Devices (MUTCD). It also features geospatial data to localize signs using their GPS coordinates. Benchmarks are presented to assess the performance of state-of-the-art deep learning based detectors.