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Description

In this study, we focus on automating road marking extraction from the HDOT MLS point cloud database, managed by Mandli. Mandli is a company specializing in highway data collection, including LiDAR. Mandli has cooperated with various Department of Transportation throughout the United States. Here, we focus on infrastructure elements related to non-motorized travel modes, supporting the ongoing Complete Streets efforts in Hawaii. Point cloud data include different colors that represent differences in elevation and intensity values. Based on a visual inspection, road markings can be observed within these point clouds. The long-term objective of this study is to develop a framework and approach for automating the detection of these infrastructure elements, based on deep learning approaches. For this project, a YOLOv5 (You Only Look Once version 5) image object detection model was trained with the HDOT point cloud data. YOLO is a family of deep learning models designed for fast object detection; the latest published version is the 5th version. The focus here is on non-motorized objects, such as crosswalks, bike lanes and bike boxes. The same approach can be extended to other markings as well, which we plan for subsequent studies.

Publication Date

7-17-2024

Keywords

safety, pedestrian, LiDAR

Handle

http://hdl.handle.net/11122/15289

INCORPORATING USE INSPIRED DESIGN IN PROVIDING SAFE TRANSPORTATION INFRASTRUCTURE FOR RITI COMMUNITIES

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