University of Essex and University of Birmingham researchers have published the Rain Fog Snow (RFS) dataset—which contains over 3,000 images of the weather conditions—to improve the ability of computer vision models to detect weather conditions. Vehicles often rely on sensors and human assistance to detect weather and road conditions, however, it can be challenging to develop effective algorithms to do so effectively. Improving the ability of these algorithms to detect weather conditions could enable semi-autonomous and autonomous vehicles to better react to dangerous conditions and increase road safety.
Detecting Weather Conditions On the Road
Michael McLaughlin is a research assistant at the Center for Data Innovation. He previously worked at Oracle and held internships at USA TODAY and in local government. Prior to joining the Center for Data Innovation, Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He is currently pursuing his Master’s in Communication at Stanford University, specializing in Data Journalism.
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