Abstract
Automatic traffic sign inventory and simultaneous condition analysis can be used to improve road maintenance processes, decrease maintenance costs, and produce up-to-date information for future intelligent driving systems. The goal of this research is to combine automatic traffic sign detection and classification with traffic sign inventory and condition analysis. This paper considers the very challenging problem of traffic sign condition analysis which is currently performed manually by experts. The manual evaluation is time-consuming, expensive, and subjective. We propose a machine vision based method to determine the condition category of each detected sign. A new dataset containing close to 400 traffic signs with condition category annotations has been specifically collected for this research since there was no suitable data available. The experimental results indicate that the average performance of the method is close to the human performance.
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The authors thank Finnish Transport Agency (FTA) for funding the TrafficVision research project.
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Hienonen, P., Lensu, L., Melander, M., Kälviäinen, H. (2017). Towards Condition Analysis for Machine Vision Based Traffic Sign Inventory. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_19
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