Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN

Zhang, Youcheng; Lu, Zongqing; Ma, Di; Xue, Jing-Hao; Liao, Qingmin

doi:10.1109/tits.2020.2971728

Cited by 50 publications

(18 citation statements)

References 26 publications

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“…The algorithm shows good reliability and robustness in nighttime lane line detection. Document [87] proposes a more powerful network called Ripple-GAN, by integrating Ripple Lane Line Detection Network (RiLLD-Net), confrontation training of Wasserstein generative adversarial networks, and…”

Section: Application Scenariomentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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Section: Application Scenariomentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…The optimization objectives of EL-GAN thus contain that of the original GAN and an item that measures the pairing difference, as shown in Table IV (6). In order to handle complex traffic scenes, such as lane markings are obscured or defective, [98] designs Ripple-GAN blending the ideas of feature fusion, Wasserstein generative adversarial training and multi-target segmentation. Experiments showed that Ripple-GAN achieves excellent performance, especially when lane marking information is incomplete.…”

Section: A Deep Architecture Focusing On Lane Marking Structurementioning

confidence: 99%

“…Both [59] and [60] use ERFNet as skeleton network, but [60] has lower FPS as two cascaded CNNs are included. [98] has excellent detection performance but its network consists of basic convolution layers without paying attention to improving network computing efficiency.…”

Section: Deep Architecture Focusing On Efficient Calculationmentioning

confidence: 99%

“…The performance comparison on Tusimple is shown in Table VII, ordered by the F1 scores. In lane marking segmentation algorithms, [98] and [53] use GAN to learn the distribution of lane marking, making the algorithm have a smaller FP and achieve the excellent F1 score. For some algorithms based on multiple network frameworks, the best performance has been selected for display.…”

Section: Performance Comparisonmentioning

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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“…Thus, automatic means are necessary for accurately extracting road segments from highresolution remote sensing imagery [8]. Machine learning-based approaches have recently demonstrated significant successes in the fields of image segmentation [9,10], object detection [11,12], and image classification [13,14]. For example, in a study conducted by [15], a road detection method using maximum likelihood technique, morphological operators and Random Sample Consensus (RANSAC) has been proposed to identify the road network from Quickbird images.…”

Section: Introductionmentioning

confidence: 99%

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

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Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing highresolution remote sensing imagery. In this study, we propose a generative adversarial network (GAN)-based deep learning approach for road segmentation from high-resolution aerial imagery. In the generative part of the presented GAN approach, we use a modified UNet model (MUNet) to obtain a high-resolution segmentation map of the road network. In combination with simple pre-processing comprising edge-preserving filtering, the proposed approach offers a significant improvement in road network segmentation compared with prior approaches. In experiments conducted on the Massachusetts road image dataset, the proposed approach achieves 91.54% precision and 92.92% recall, which correspond to a Mathews correlation coefficient (MCC) of 91.13%, a Mean intersection over union (MIOU) of 87.43% and a F1-score of 92.20%. Comparisons demonstrate that the proposed GAN framework outperforms prior CNN-based approaches and is particularly effective in preserving edge information.

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Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN

Cited by 50 publications

References 26 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

Deep Learning in Lane Marking Detection: A Survey

Improving Road Semantic Segmentation Using Generative Adversarial Network

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