The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images. Published in the journal of Computers, Environment and Urban Systems, the study shows the system detects signs with near 96 per cent accuracy, identifies their type with near 98 per cent accuracy and can record their precise geo-location from the 2D images.
“The proof-of-concept model was trained to see ‘stop’ and ‘give way’ (yield) signs, but could be trained to identify many other inputs and was easily scalable for use by local governments and traffic authorities,” said the study lead author Andrew Campbell from RMIT University in Australia.
Municipal authorities spend a large amount of time and money monitoring and recording the geo-location of traffic infrastructure manually, a task which also exposes workers to unnecessary traffic risks.
“By using free and open source tools, we’ve developed a fully automated system for doing that job, and doing it more accurately,” Campbell said.
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