3D-LaneNet: End-to-End 3D Multiple Lane Detection

Garnett, Noa; Cohen, Rafi; Pe'er, Tomer; Lahav, Roee; Levi, Dan

doi:10.1109/iccv.2019.00301

Cited by 150 publications

(117 citation statements)

References 26 publications

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“…However, these works place significant demands on dataset preparation. 3D vision has also been introduced to the lane boundary detection domain [21,22] with reasonable results, but a major problem remains the costliness and rarity of both devices and datasets. Lee et al [23] proposed a new way of predicting vanishing points to guide lane boundary prediction, giving a new approach to object-guided lane boundary segmentation.…”

Section: Neural-network-based Methodsmentioning

confidence: 99%

SpinNet: Spinning convolutional network for lane boundary detection

et al. 2019

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In this paper, we propose a simple but effective framework for lane boundary detection, called SpinNet. Considering that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive, therefore, analyzing and collecting global context information is crucial for lane boundary detection. To this end, we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global perspective. To extract features in narrow strip-shaped fields, we adopt stripshaped convolutions with kernels which have 1 × n or n × 1 shape in the spinning convolution layer. To tackle the problem of that straight strip-shaped convolutions are only able to extract features in vertical or horizontal directions, we introduce the concept of feature map rotation to allow the convolutions to be applied in multiple directions so that more information can be collected concerning a whole lane boundary. Moreover, unlike most existing lane boundary detectors, which extract lane boundaries from segmentation masks, our lane boundary parameterization branch predicts a curve expression for the lane boundary for each pixel in the output feature map. And the network utilizes this information to predict the weights of the curve, to better form the final lane boundaries. Our framework is easy to implement and end-to-end trainable. Experiments show that our proposed SpinNet outperforms state-of-the-art methods.

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Section: Neural-network-based Methodsmentioning

confidence: 99%

SpinNet: Spinning convolutional network for lane boundary detection

et al. 2019

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“…2) Regression losses: When the output of a deep learner is expected to be continuous, regression losses are more suitable compared with those classification ones mentioned in section III-A1. In lane marking detection, the most commonly used regression losses are the mean squared error (MSE) [53], [59], [64], [75], [77]- [79], mean absolute error (MAE) [54], [79], [80], and Huber loss defined as…”

Section: Representative Objective Functionsmentioning

confidence: 99%

“…All algorithms mentioned above conduct 2D lane marking detection. We note that some algorithms have carried out related research with the aim of 3D lane marking positioning, for instance, [79] and 3D-LaneNet [80].…”

Section: Deep Architecture Focusing On Efficient Calculationmentioning

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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“…In addition to using Convolution Neural Network (CNN) and front view to model, Garnett et al [7] not only input the bird's eye view into the CNN, but also took advantage of 3D modeling method to deal with the complex fork road and curve situation. Liu et al [8] replaced CNN model with Transformer and greatly improved both the accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection Based on Adaptive Network of Receptive Field

Cai

Zhang

Pan

2021

Security and Communication Networks

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The difficulty of lane detection lies in the imbalance of the number of target pixels and background pixels. The sparse target distribution misleads the neural network to pay more attention to background segmentation in order to obtain a better loss convergence result. This makes it difficult for some models to detect lane line pixels and leads to the training fail (unable to output useful lane information). Increasing receptive field properly can enlarge the sphere of action between pixels, so as to restrain this trouble. Moreover, the interference information and noise existing in the real environment increase the difficulty of lane classification, such as vehicle occlusion, car glass reflection, and tree shadow. In this paper, we do think that the features obtained by the reasonable combination of receptive fields can help avoid oversegmentation of the image, so that most of the interference information can be filtered out. Based on this idea, Adaptive Receptive Field Net (ARFNet) is proposed to solve the problem of receptive field combination with the help of multireceptive field aggregation layers and scoring mechanism. This paper explains the working principle of ARFNet and analyzes several results of experiments, which are carried out to adjust network structure parameters in order to get better effects in the CuLane dataset testing.

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3D-LaneNet: End-to-End 3D Multiple Lane Detection

Cited by 150 publications

References 26 publications

SpinNet: Spinning convolutional network for lane boundary detection

SpinNet: Spinning convolutional network for lane boundary detection

Deep Learning in Lane Marking Detection: A Survey

Lane Detection Based on Adaptive Network of Receptive Field

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