A Review of Deep Learning-Based Lane Detection Methods

Ting Liu; Megat Norulazmi Megat Mohamed Noor; Mohammad Faizuddin Bin Md Noor

doi:10.63944/6z3.aia

A Review of Deep Learning-Based Lane Detection Methods

Ting Liu (Primary Contact)

Department of Computer Engineering Technology, Universiti Kuala Lumpur – Malaysian Institute of Information Technology

Megat Norulazmi Megat Mohamed Noor (Author)

Department of Computer Engineering Technology, Malaysian Institute of Information Technology

Mohammad Faizuddin Bin Md Noor (Author)

Department of CACS - Center for Alumni & Career Services, Universiti Kuala Lumpur – Malaysian Institute of Information Technology

DOI:https://doi.org/10.63944/6z3.aia

Keywords:

lane line detection, Instance segmentation, deep learning

Published

31-03-2026

Abstract: Lane detection serves as a critical environmental perception application designed to identify lane markings utilizing onboard cameras or LiDAR systems. In recent years, concurrent with the advancement and deployment of computer vision applications, lane detection tasks have garnered substantial attention, leading to the emergence of a diverse array of lane detection methodologies. This article presents a comprehensive overview of lane detection techniques predicated on the acquisition of two-dimensional images via onboard cameras. Initially, it outlines the fundamental task of lane detection. Subsequently, it introduces datasets pertinent to lane detection. Following this, it delineates both traditional lane detection approaches and those grounded in deep learning. Ultimately, it provides an in-depth discussion of deep learning-based methods. Lane detection methodologies leveraging deep learning can be systematically categorized into four primary types: segmentation-based approaches, detection-based approaches, parameter curve-based approaches, and key point-based approaches.

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A Review of Deep Learning-Based Lane Detection Methods

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