Driving with Deep Learning: A Robust Traffic Sign Detection and Classification System

Intelligent Transport Systems, such as self-driving automobiles, represent a prime example of applications utilizing traffic sign recognition technology. The increasing demand for autonomous vehicles highlights a significant challenge: their ability to adhere to traffic regulations without human intervention for road safety. As technological advancements pave the way for increased automation, this study addresses the detection and identification of traffic sign boards under varying conditions, including lighting, direction, and diverse weather scenarios. Focusing on scenarios where the traffic sign is the sole object in an image with a minimal background, image processing techniques are employed for traffic sign recognition. Furthermore, the identified and classified sign is associated with its specific geospatial coordinates, using the device's location data, and subsequently stored in a database. This enables tracking of updates or changes to traffic signs along a designated route or area, with original information being replaced or overwritten upon any alteration at a particular location.