Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system
Abstract Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-07-01
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| Series: | Computational Urban Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43762-023-00098-7 |
| _version_ | 1797778949375262720 |
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| author | Kun Xie Huiming Sun Xiaomeng Dong Hong Yang Hongkai Yu |
| author_facet | Kun Xie Huiming Sun Xiaomeng Dong Hong Yang Hongkai Yu |
| author_sort | Kun Xie |
| collection | DOAJ |
| description | Abstract Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance. |
| first_indexed | 2024-03-12T23:24:53Z |
| format | Article |
| id | doaj.art-16866b9a5f5f41eb879ba20152b56dd9 |
| institution | Directory Open Access Journal |
| issn | 2730-6852 |
| language | English |
| last_indexed | 2024-03-12T23:24:53Z |
| publishDate | 2023-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj.art-16866b9a5f5f41eb879ba20152b56dd92023-07-16T11:12:09ZengSpringerComputational Urban Science2730-68522023-07-013111610.1007/s43762-023-00098-7Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered systemKun Xie0Huiming Sun1Xiaomeng Dong2Hong Yang3Hongkai Yu4Department of Civil and Environmental Engineering, Old Dominion UniversityDepartment of Electrical Engineering and Computer Science, Cleveland State UniversityDepartment of Civil and Environmental Engineering, Old Dominion UniversityDepartment of Electrical and Computer Engineering, Old Dominion UniversityDepartment of Electrical Engineering and Computer Science, Cleveland State UniversityAbstract Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance.https://doi.org/10.1007/s43762-023-00098-7Intersection markingsArtificial intelligenceData acquisitionDegradation condition assessmentInfrastructure management |
| spellingShingle | Kun Xie Huiming Sun Xiaomeng Dong Hong Yang Hongkai Yu Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system Computational Urban Science Intersection markings Artificial intelligence Data acquisition Degradation condition assessment Infrastructure management |
| title | Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system |
| title_full | Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system |
| title_fullStr | Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system |
| title_full_unstemmed | Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system |
| title_short | Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system |
| title_sort | automating intersection marking data collection and condition assessment at scale with an artificial intelligence powered system |
| topic | Intersection markings Artificial intelligence Data acquisition Degradation condition assessment Infrastructure management |
| url | https://doi.org/10.1007/s43762-023-00098-7 |
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