The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 507–514, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-507-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 507–514, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-507-2020

  21 Aug 2020

21 Aug 2020

LIDAR-BASED LANE MARKING EXTRACTION THROUGH INTENSITY THRESHOLDING AND DEEP LEARNING APPROACHES: A PAVEMENT-BASED ASSESSMENT

Y.-T. Cheng, A. Patel, D. Bullock, and A. Habib Y.-T. Cheng et al.
  • Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907, USA

Keywords: Asphalt Pavement, Concrete Pavement, Lane Marking Extraction, Intensity Normalization, Deep Learning, Automated Labeling

Abstract. With the rapid development of autonomous vehicles (AV) and high-definition (HD) maps, up-to-date lane marking information is necessary. Over the years, several lane marking extraction approaches have been proposed with many of them based on accurate and dense Light Detection and Ranging (LiDAR) point cloud data collected by mobile mapping systems (MMS). This study proposes a normalized intensity thresholding strategy and a deep learning strategy with automatically generated labels. The former extracts lane markings directly from LiDAR point clouds while the latter utilizes 2D intensity images generated from the LiDAR point cloud. Additionally, the proposed approaches are also compared with state-of-the-art strategies such as original intensity thresholding and a deep learning approach based on manually established labels. Finally, each strategy is evaluated in asphalt and concrete pavements separately to assess their sensitivity to the nature of pavement surface. The results show that the deep learning model trained with automatically generated labels performs the best in both asphalt and concrete pavement area with an F1-score of 84.9% and 85.1%. In asphalt pavement area, original intensity thresholding strategy shows a lane marking extraction performance comparable to the other strategies while in concrete pavement area, it is significantly poor with an F1-score of 65.1%. Between the proposed normalized intensity thresholding and deep learning model trained with manually labeled data, the former performs better in asphalt pavement area while the latter obtains better results in concrete pavements.