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Having a comprehensive, high-quality dataset of road sign annotation iscritical to the success of AI-based Road Sign Recognition (RSR) systems. Inpractice, annotators often face difficulties in learning road sign systems ofdifferent countries; hence, the tasks are often time-consuming and produce poorresults. We propose a novel approach using knowledge graphs and a machinelearning algorithm - variational prototyping-encoder (VPE) - to assist humanannotators in classifying road signs effectively. Annotators can query the RoadSign Knowledge Graph using visual attributes and receive closest matchingcandidates suggested by the VPE model. The VPE model uses the candidates fromthe knowledge graph and a real sign image patch as inputs. We show that ourknowledge graph approach can reduce sign search space by 98.9%. Furthermore,with VPE, our system can propose the correct single candidate for 75% of signsin the tested datasets, eliminating the human search effort entirely in thosecases.

Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning