Version 1
: Received: 29 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (04:28:21 CEST)
How to cite:
Antwi, R. B.; Kimollo, M.; Ozguven, E. E.; Sando, T.; Moses, R.; Dulebenets, M. A. Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways. Preprints2024, 2024050021. https://doi.org/10.20944/preprints202405.0021.v1
Antwi, R. B.; Kimollo, M.; Ozguven, E. E.; Sando, T.; Moses, R.; Dulebenets, M. A. Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways. Preprints 2024, 2024050021. https://doi.org/10.20944/preprints202405.0021.v1
Antwi, R. B.; Kimollo, M.; Ozguven, E. E.; Sando, T.; Moses, R.; Dulebenets, M. A. Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways. Preprints2024, 2024050021. https://doi.org/10.20944/preprints202405.0021.v1
APA Style
Antwi, R. B., Kimollo, M., Ozguven, E. E., Sando, T., Moses, R., & Dulebenets, M. A. (2024). Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways. Preprints. https://doi.org/10.20944/preprints202405.0021.v1
Chicago/Turabian Style
Antwi, R. B., Ren Moses and Maxim A. Dulebenets. 2024 "Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways" Preprints. https://doi.org/10.20944/preprints202405.0021.v1
Abstract
Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida's public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3,026 left turn, 1,210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.
Keywords
Turning lanes; Deep Learning; Roadway Characteristic Index (RCI); Pavement Markings; Machine Learning (ML); Roadway Geometry Features
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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