Journal ArticleDOI
Lane Detection of Curving Road for Structural Highway With Straight-Curve Model on Vision
TLDR
Experiments show that this curve detection algorithm can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.Abstract:
Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.read more
Citations
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Journal ArticleDOI
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
TL;DR: This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations.
Journal ArticleDOI
Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN
TL;DR: Experiments show that, especially for complex or obscured lane lines, Ripple-GAN can produce a superior detection performance to other state-of-the-art methods.
Journal ArticleDOI
Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks
TL;DR: This paper aims to conduct gangue segmentation using a U-shape fully convolutional neural network (U-Net) trained to segment gangue from raw coal images collected under complex environmental conditions.
Journal ArticleDOI
Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure
TL;DR: In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF) and an illumination invariance method is presented to detect lane markers under different light conditions.
Journal ArticleDOI
Laser Stripe Center Detection Under the Condition of Uneven Scattering Metal Surface for Geometric Measurement
TL;DR: By analyzing the laser scattering and reflection models, a set of light stripe center’s subpixel extraction methods which has strong robustness is proposed and can effectively eliminate the interference of flash point noise and hasStrong robustness.
References
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Proceedings ArticleDOI
Vanishing point detection for road detection
TL;DR: This paper decomposes the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based on the detected vanishing point.
Journal ArticleDOI
Real-time illumination invariant lane detection for lane departure warning system
TL;DR: This paper proposes a real-time and illumination invariant lane detection method for lane departure warning system that works well in various illumination conditions such as in bad weather conditions and at night time.
Journal ArticleDOI
Gradient-Enhancing Conversion for Illumination-Robust Lane Detection
TL;DR: A gradient-enhancing conversion method that produces a new gray-level image from an RGB color image based on linear discriminant analysis for illumination-robust lane detection and a novel lane detection algorithm, which uses the proposed conversion method, adaptive Canny edge detector, Hough transform, and curve model fitting method.
Journal ArticleDOI
A Learning Approach Towards Detection and Tracking of Lane Markings
TL;DR: A pixel-hierarchy feature descriptor is proposed to model the contextual information shared by lane markings with the surrounding road region and a robust boosting algorithm to select relevant contextual features for detecting lane markings is proposed.
Book ChapterDOI
Robust Lane Detection Based On Convolutional Neural Network and Random Sample Consensus
Jihun Kim,Minho Lee +1 more
TL;DR: A robust lane detection method based on the combined convolutional neural network (CNN) with random sample consensus (RANSAC) algorithm is introduced and the performance is found to be better than other formal line detection algorithms such as RANSAC and hough transform.