A Benchmark for Detection and Recognition of Bangladeshi Traffic Signs in Real-world Images

Abstract

Traffic sign detection is an indispensable part of autonomous driving and transportation safety systems. However, the accurate detection and recognition of traffic signs remain challenging, especially under extreme conditions, such as various weather and geo-social features. Though a lot of work has been done in the domain of Traffic Sign Detection and Recognition (TSDR) systems, only a few of them focus on a dataset that comprises the real-world challenges. Moreover, in the context of Bangladesh, there is no well-defined public dataset, let alone one that focuses on real-life challenges. The geo-social features of Bangladesh add some unique challenges that are not seen in most parts of the world. This proposal aims to address the lack of quality Bangla traffic sign detection dataset. To accomplish this task, traffic sign images will be extracted from videos collected from Bangladeshi roads. A performance benchmark will be presented by applying state-of-the-art methods to the said dataset. Using the best-performing method, an autonomous driver notification system will be developed to alert the drivers on the go.