Neural network-based traffic sign recognition in 360° images for semi-automatic road maintenance inventory

Abstract

Traffic sign inventory is an important part of road safety and traffic management. Road maintenance services perform this task with periodical on-site inspections, monitoring the physical presence and integrity of each object. Due to the increasing complexity of road infrastructure and the high number of objects, this task is usually time-consuming. Object detection based on artificial neural networks and state-of-the-art 360° camera equipment may be used to enhance the work of road maintenance services. By automatically capturing images during mandatory inspection drives, detecting traffic signs in those images and linking them with corresponding database entries, semi-automatic inventory and off-site inspections will be possible. This paper describes a system for semi-automatic traffic sign inventory that, in contrast to other approaches, automatically detects and classifies various traffic sign types in 360° images. It then references those signs in an existing inventory database, using GPS and distance estimation and allows for a virtual reality like off-site inspections with a 360° view.