Computer vision-based model for detecting turning lane features on Florida's public roadways
arxiv(2024)
摘要
Efficient and current roadway geometry data collection is critical to
transportation agencies in road planning, maintenance, design, and
rehabilitation. Data collection methods are divided into land-based and
aerial-based. Land-based methods for extensive highway networks are tedious,
costly, pose safety risks. Therefore, there is the need for efficient, safe,
and economical data acquisition methodologies. The rise of computer vision and
object detection technologies have made automated extraction of roadway
geometry features feasible. This study detects roadway features on Florida's
public roads from high-resolution aerial images using AI. The developed model
achieved an average accuracy of 80.4 percent when compared with ground truth
data. The extracted roadway geometry data can be integrated with crash and
traffic data to provide valuable insights to policymakers and roadway users.
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