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 analyst at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
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