A review of lane detection methods based on deep learning. (March 2021)

• Record Type: Journal Article
• Authors: Tang, Jigang; Li, Songbin; Liu, Peng
• Is Part Of: Pattern recognition. Volume 111(2021)
• Publication Date: 2021-03
• Subjects: Lane detection -- Deep learning -- Semantic segmentation -- Instance segmentation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107623
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This work is the _rst overall review of recent deep learning-based lane detection methods. Detailed description of representive methods from perpective of computer vision and pattern recognition. Detailed description of convolution neural networks' architectures and loss functions that used in lanes detector. Advantages of deep learning-based methods compared with traditional heuristic recognition-based methods. Current challenges of existing deep learning-based methods and some possible directions to solve the problems. Abstract: Lane detection is an application of environmental perception, which aims to detect lane areas or lane lines by camera or lidar. In recent years, gratifying progress has been made in detection accuracy. To the best of our knowledge, this paper is the first attempt to make a comprehensive review of vision-based lane detection methods. First, we introduce the background of lane detection, including traditional lane detection methods and related deep learning methods. Second, we group the existing lane detection methods into two categories: two-step and one-step methods. Around the above summary, we introduce lane detection methods from the following two perspectives: (1) network architectures, including classification and object detection-based methods, end-to-end image-segmentation based methods, and some optimization strategies; (2) related loss functions. For each method, its contributions and weaknesses are introduced. Then, a brief comparison of representative methods is presented. Finally, we conclude this survey with some current challenges, such as expensive computation and the lack of generalization. And we point out some directions to be further explored in the future, that is, semi-supervised learning, meta-learning and neural architecture search, etc.