Real-Time Lane Detection Based on Deep Learning

Baek, Sun-Woo; Kim, Myeong-Jun; Suddamalla, Upendra; Wong, Anthony; Lee, Banghyon; Kim, Jung-Ha

doi:10.1007/s42835-021-00902-6

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…It is the most often used dataset among academics in lane detecting studies. Tusimple has been used to test many algorithms [1], [5], [20], [21], as it was the largest lane detection dataset before 2018. This dataset contains 3626 training photos and 2782 testing images on highway roads.…”

Section: ) Culane Datasetmentioning

confidence: 99%

“…Caltech is the second most used dataset for lane detection. The Caltech Lanes dataset contains four video sequences (or sub-datasets) in urban settings, totaling 1225 images, which have been used in some previous research [6], [9], [13], [21], [28]. Aside from that, the Kitti and CuLane datasets are well-known online datasets for lane detecting tasks.…”

Section: ) Culane Datasetmentioning

confidence: 99%

“…The Kitti road has two sorts of annotations: road segmentation, which covers all lanes, and ego-lane, which designates the lane in which the car is presently moving. For examples of past research that used this dataset, see [21], [28], and [32]. CULane, on the other hand, features various challenging driving circumstances, including congested roads or highways with low lighting.…”

Section: ) Culane Datasetmentioning

confidence: 99%

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

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Section: ) Culane Datasetmentioning

confidence: 99%

Section: ) Culane Datasetmentioning

confidence: 99%

Section: ) Culane Datasetmentioning

confidence: 99%

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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“…Experiments on CULane and TuSimple demonstrate the effectiveness of the model. A network with structural priors is proposed in [2]. By improving the convolutional structure and loss function, this model achieves close results on CULane with a much lower computational load than state-of-the-art methods.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Experiments on TuSimple and CULane prove that this model can find a better balance between performance and cost. A lightweight lane detection model is proposed in [2]. The authors evaluate several encoder and decoder combinations and verify the accuracy and speed on TuSimple.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Lane Detection with Deep Learning: Methods and Datasets

2023

ITC

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Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.

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LaneNet++: Uncertainty-Aware Lane Detection for Autonomous Vehicle

Basavaraj,

Suddamalla,

2023

Lecture Notes in Computer Science

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Real-Time Lane Detection Based on Deep Learning

Cited by 12 publications

References 21 publications

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection with Deep Learning: Methods and Datasets

LaneNet++: Uncertainty-Aware Lane Detection for Autonomous Vehicle

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