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Road mark detection based on local point cloud projection and R-FCN
Author(s): Tianxiong Lv; Huimin Ma
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Paper Abstract

For the road mark in the presence of abrasion, adhesion, and occlusion, it is difficult to accurately locate the road mark by typical point cloud clustering and segmentation method. An automatic road mark detection method based on local point cloud projection and 2D deep learning object detection is proposed. Firstly, the local ground point cloud regions are obtained by prior information; Secondly, we perform orthogonal projection on the point cloud to project the point cloud into two-dimensional image by affine transformation and use R-FCN detection method to detect the road mark in the image. The holes on the image are filtered by local maximum filtering. A line-by-line search strategy based on maximum reflection rate is proposed to refine the detected bounding boxes to improve the detection accuracy; Finally, we implement inverse affine transformation and local coordinate search to restore the road mark coordinates from twodimensional image to 3D point cloud. In the point cloud dataset collected from China highway, our experimental results show that the proposed method can achieve road mark detection under complex scenes such as occlusion, abrasion and adhesion. Comparing with other methods, the detection recall, accuracy and efficiency have been improved greatly.

Paper Details

Date Published: 6 May 2019
PDF: 8 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693S (6 May 2019); doi: 10.1117/12.2524376
Show Author Affiliations
Tianxiong Lv, Tsinghua Univ. (China)
Huimin Ma, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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