Lane Boundary Detection Algorithm Based on Vector Fuzzy Connectedness

Fang, Lingling; Wang, Xianghai

doi:10.1007/s12559-017-9483-3

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…Unlike image processing techniques, Machine Learning (ML) techniques can extract and learn features for classification. ML techniques have been used in prior AV models [23], [24], and can perform detection and segmentation tasks in vast scenarios. A method to detect road lane boundaries has been recommended by Fang and Wang [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…ML techniques have been used in prior AV models [23], [24], and can perform detection and segmentation tasks in vast scenarios. A method to detect road lane boundaries has been recommended by Fang and Wang [23]. The authors propose a method named Vector Fuzzy Connectedness (VFC).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers have proposed different techniques to address road detection and segmentation [15]- [22]. Some of these are based on Digital Image Processing (DIP) [17], [19], [22], whereas some use Machine Learning (ML) techniques [23], [24] to address the problem.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

et al. 2020

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This study presents a shallow and robust road segmentation model. The computer-aided real-time applications, like driver assistance, require real-time and accurate processing. Current studies use Deep Convolutional Neural Networks (DCNN) for road segmentation. However, DCNN requires high computational power and lots of labeled data to learn abstract features for deeper layers. The deeper the layer is, the more abstract information it tends to learn. Moreover, the prediction time of the DCNN network is an important aspect of autonomous vehicles. To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations of the input image. Gradient information of the input image is used as additional channels to enhance the learning process of the proposed deep learning architecture. The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions. The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as compared to baseline CNN consisting of only RGB inputs. Furthermore, the comparison of the proposed method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme performs better while being more efficient during training and evaluation. Unlike traditional segmentation techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only the encoder network. The proposed MVS-CNN has been trained and validated with two well-known datasets: the KITTI Vision Benchmark and the Cityscapes dataset. The results have been compared with the state-ofthe-art deep learning architectures. The proposed MVS-CNN outperforms and shows supremacy in terms of model accuracy, processing time, and segmentation accuracy. Based on the experimental results, the proposed architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

et al. 2020

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“…Employing the averaging of the pixel values of preceeding frames approach is introduced in [2] to improve the low-quality lane markers. Noise smoothing Gaussian filter to remove the noise of the mounted camera is dealt in [15]. The image pyramid approach is adopted in [16] to diminish the details and to present the high-frequency data.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Ghanem

Kanungo

Panda

et al. 2021

Complex Intell. Syst.

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Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

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“…Phanindra et al presented a method for detecting obstacles and lane using the data collected by a LIDAR sensor and a fisheye camera [10]. A vector fuzzy connectedness-based algorithm has been proposed by Lingling Fang et al to detect the boundary of the lane using the captured images by the camera [11].…”

Section: Introductionmentioning

confidence: 99%

Game Theory-Based Autonomous Vehicle Control Via Image Processing

Zortul¹,

Waqar²,

Ersoy³

2020

Autonomous Vehicle and Smart Traffic

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Self-driven vehicles slowly but surely are making the transition from a distant future technology to current luxury by slowly becoming a part of our everyday life. Due to their self-driving ability, they are making our travels efficient. However, they are still a work in progress as they require many software and hardware-based improvements. To address the software part of this issue, an image processingbased solution has been proposed in this study. The algorithm estimates the real-time positions and predicts the possible interaction of the objects, such as other moving vehicles, present in the vicinity of the driven autonomous vehicle in determined environmental conditions. Cameras and related peripheral present on autonomous vehicles are used to obtain data related to the real-time situation for predicting and preventing possible hazardous events, such as accidents, using these data.

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Lane Boundary Detection Algorithm Based on Vector Fuzzy Connectedness

Cited by 10 publications

References 19 publications

Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Game Theory-Based Autonomous Vehicle Control Via Image Processing

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