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Article

Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

1
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 29 March 2020 / Revised: 23 April 2020 / Accepted: 24 April 2020 / Published: 27 April 2020
Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause. View Full-Text
Keywords: lane marking extraction; lane width estimation; intensity normalization; deep learning; mobile mapping systems; automated labeling; lane marking condition lane marking extraction; lane width estimation; intensity normalization; deep learning; mobile mapping systems; automated labeling; lane marking condition
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MDPI and ACS Style

Cheng, Y.-T.; Patel, A.; Wen, C.; Bullock, D.; Habib, A. Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds. Remote Sens. 2020, 12, 1379. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091379

AMA Style

Cheng Y-T, Patel A, Wen C, Bullock D, Habib A. Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds. Remote Sensing. 2020; 12(9):1379. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091379

Chicago/Turabian Style

Cheng, Yi-Ting, Ankit Patel, Chenglu Wen, Darcy Bullock, and Ayman Habib. 2020. "Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds" Remote Sensing 12, no. 9: 1379. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091379

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