End-to-End Lane Marker Detection via Row-wise Classification

Yoo, Seungwoo; Lee, Hee-Seok; Myeong, Heesoo; Yun, Sungrack; Park, Hyoungwoo; Cho, Janghoon; Kim, Duck Hoon

doi:10.1109/cvprw50498.2020.00511

Cited by 144 publications

(93 citation statements)

References 20 publications

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“…To the best of our knowledge, there is no one-fits-all objective function to train neural networks for different purposes, and the context of application is of the key consideration on which a suitable choice is made. In this case, for different steps or constraints imposed for the same aim, it is very common to find in recent literature [47], [65]- [67] that more than one kind of objective function is adopted throughout the whole procedure. Because objective functions are among the most important factors affecting the performance of deep learning algorithms, more comprehensive reasons for the choice and more detailed experimental validity are expected for future research in the lane marking detection community.…”

Section: B Objective Functions For Deep Unsupervised / Semisupervised Learning Modelsmentioning

confidence: 99%

“…[50] achieves more efficient vanishing lane marking prediction by deep learning with the assistance of an adaptive dark-light-dark method. [65] designs horizontal reduction modules to treat lane marking detection as a line-by-line classification problem, making use of the inherent shape of lane markings. Neural Architecture Search (NAS) is a technology for automatically designing high-performance network structures based on sampled data through algorithms.…”

Section: A Deep Architecture Focusing On Lane Marking Structurementioning

confidence: 99%

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Deep Learning in Lane Marking Detection: A Survey

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

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Section: B Objective Functions For Deep Unsupervised / Semisupervised Learning Modelsmentioning

confidence: 99%

Section: A Deep Architecture Focusing On Lane Marking Structurementioning

confidence: 99%

Deep Learning in Lane Marking Detection: A Survey

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Similar to other visual perception tasks, the common classification of lane detection methods is a traditional lane detection based on image processing and machine learning [21–23], and deep learning methods based on CNN [6, 24–29] or RNN [3]. For both of them, the feature extraction is the key, and the latter one has a high data dependency and high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…By having a review on the CNN‐based methods [3, 6, 24–29], we conclude that there are three types of them: two‐stage methods, one‐stage (end‐to‐end) methods and others. The first one can be further divided into a CNN for semantic segmentation with sophisticated post‐processing [6, 21, 24], and traditional image processing for lane detection with a CNN for lane classification [27, 29, 36].…”

Section: Related Workmentioning

confidence: 99%

“…The one‐stage method means that it directly predicts the lane markers without any post‐processing step. It includes an end‐to‐end encoder–decoder CNN method [25], a GAN‐based semantic segmentation method with an embedding loss [28], and a CNN with an attention distillation mechanism [26]. Also, there are other trials, for example Li et al [3] use a multi‐task network in the RNN architectures to deal with lane and road marking detection simultaneously.…”

Section: Related Workmentioning

confidence: 99%

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Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Xiong

Liu

et al. 2020

IET intell. transp. syst

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Multi‐lane recognition using the YOLO network with rotatable bounding boxes

Park

2023

J Soc Info Display

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Currently, driver assistance and autonomous driving functions are emerging as essential convenience functions in automobiles. For autonomous driving, fast and accurate lane recognition is required, along with driving environment recognition. The recognized lanes must be divided into ego and left-and rightside lanes. Among deep learning, the You Only Look Once (YOLO) network is widely known as a fast and accurate object detection technique. The general methods are not robust to angle variations of the objects because of the use of a traditional bounding box, a rotation variant structure for locating rotated objects. The rotatable bounding box (RBBox) can effectively handle situations where the orientation angles of the objects are arbitrary. This study uses a YOLO approach with RBBox to recognize multi-lane accurately. The proposed method recognizes the ego lane and its surrounding lanes by accurately distinguishing them. And the proposed method shows stable multi-lane recognition performance by predicting them that exist in the images but do not exist in the ground truth of the TuSimple data set. Even compared to other lane recognition methods, it shows good competitiveness. Nevertheless, more training data and network learning are needed in a specific road environment (a lane is centered on the image).

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End-to-End Lane Marker Detection via Row-wise Classification

Cited by 144 publications

References 20 publications

Deep Learning in Lane Marking Detection: A Survey

Deep Learning in Lane Marking Detection: A Survey

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Multi‐lane recognition using the YOLO network with rotatable bounding boxes

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