Using Google Street View Images, a new program was developed to monitor street signs needing replacement or repair. The newly developed fully-automated system uses an artificial intelligence model powered by object detection to identify street signs in the freely available images. The development can save time and money while importantly keeping municipal workers safe from unnecessary exposure to traffic risks.
"(Municipal authorities) have requirements to monitor this infrastructure but currently no cheap or efficient way to do so," says Andrew Campbell, study lead author and RMIT University Geospatial Science student. "By using free and open source tools, we've now developed a fully automated system for doing that job, and doing it more accurately."
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"Tracking these signs manually by people who may not be trained geoscientists introduces human error into the database. Our system, once set up, can be used by any spatial analyst -- you just tell the system which area you want to monitor and it looks after it for you," Campbell said.
The system can detect signs with an estimated 96% accuracy, identifies the type of sign with a near 98% accuracy and can record precise geolocation from 2D images. The system can also be trained for other inputs for use by local governments and traffic authorities. Findings were reported in the journal of Computers, Environment and Urban Systems.
"This imagery is critical for local governments in monitoring and managing assets and with the huge amount of geospatial applications flourishing, this information will only become more valuable," says RMIT geospatial scientist and project co-lead, Dr Chayn Sun. "Ours is one of several early applications for this to meet a specific industry need but a whole lot more will emerge in coming years. Where footage is already being gathered, our research can provide councils with an economical tool to drive insights and data from this existing resource.”
Source: Science Daily
