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https://doi.org/10.1007/978-3-319-46484-8_29

has been cited by the following article:

• TITLE: Lane Line Detection Based on Improved PINet

  AUTHORS: Xueyan Jiao, Yiqiao Lin, Lei Zhao

  KEYWORDS: Lane Line Detection, Instance Segmentation, Accuracy, Real Time

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.3, March 31, 2023

  ABSTRACT: Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines; images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines; and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.