Computer vision-based model for detecting turning lane features on Florida’s public roadways from aerial images-Reference-Cited by-同舟云学术

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Computer vision-based model for detecting turning lane features on Florida’s public roadways from aerial images

Published:2024-08-08 Issue: Volume: Page:1-32
ISSN:0308-1060
Container-title:Transportation Planning and Technology
language:en
Short-container-title:Transportation Planning and Technology

Author:

Antwi Richard Boadu¹^ORCID,Takyi Samuel¹,Kimollo Michael²,Karaer Alican³,Ozguven Eren Erman¹,Moses Ren¹,Dulebenets Maxim A.¹,Sando Thobias²

Affiliation:

1. Department of Civil and Environmental Engineering, Florida A&M University–Florida State University College of Engineering, Florida State University, Tallahassee, FL, USA

2. School of Engineering, University of North Florida, Jacksonville, FL, USA

3. Iteris Inc., Tallahassee, FL, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081060.2024.2386614

Reference55 articles.

1. A practical approach for detection and classification of traffic signs using Convolutional Neural Networks

2. Detecting of Pavement Marking Defects Using Faster R-CNN

3. Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways

4. Antwi R. B. S. Takyi A. Karaer E. E. Ozguven M. Kimollo R. Moses and T. Sando. 2024b. “Automated GIS-Based Framework for Detecting Crosswalk Changes from Bi-Temporal High-Resolution Aerial Images.” arXiv preprint arXiv:2406.09731.

5. Detecting School Zones on Florida’s Public Roadways Using Aerial Images and Artificial Intelligence (AI2)

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