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Mutation detection for inventories of traffic signs from street-level panoramic images

Road safety is positively influenced by both adequate placement and optimal visibility of traffic signs. As their visibility degrades over time due to e.g. aging, vandalism, accidents and vegetation coverage, up-to-date inventories of traffic signs are highly attractive for preserving a high road safety. These inventories are performed in a semi-automatic fashion from street-level panoramic images, exploiting object detection and classification techniques. Next to performing inventories from scratch, these systems are also exploited for the efficient retrieval of situation changes by comparing the outcome of the automated system to a baseline inventory (e.g. performed in a previous year). This allows for specific manual interactions to the found changes, while skipping all unchanged situations, thereby resulting in a large efficiency gain. This work describes such a mutation detection approach, with special attention to re-identifying previously found signs. Preliminary results on a geographical area containing about 425 km of road show that 91.3% of the unchanged signs are re-identified, while the amount of found differences equals about 35% of the number of baseline signs. From these differences, about 50% correspond to physically changed traffic signs, next to false detections, misclassifications and missed signs. As a bonus, our approach directly results in the changed situations, which is beneficial for road sign maintenance.