Real-Time Lane Detection-Based Line Segment Detection

Mahmoud, Ahmed M.; Ehab, Loay; Reda, Mohamed; Abdel-Aleem, Mostafa; Munim, Hossam Abd El; Ghoneima, Maged; Darweesh, M. Saeed; Mostafa, Hassan

doi:10.1109/ngcas.2018.8572124

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…In most studies, such as [5], [9], [23], [25], the proposed algorithm failed to detect the lane lines in cases of unclear or covered lane markings. YenIaydin and Schmidt [6] were able to detect damaged and parallel lines in both straight and curved lanes.…”

Section: Amentioning

confidence: 99%

“…Mahmoud et al [23] proposed a real-time system capable of detecting both straight and curved lane lines using a Line Segment Detector (LSD). The authors used offline processing and the Caltech dataset to locate lane lines under different lighting conditions.…”

Section: Amentioning

confidence: 99%

“…The vertices of the triangular mask were defined, followed by linear fitting of the sides of the triangular mask. The literature [23] [24], suggests that both linear and polynomial fitting techniques yielded positive outcomes; hence, polynomial fitting was applied to obtain the coefficients of the fit. If the mask pixels fell below the predetermined threshold value, the region inside the lines was identified.…”

Section: Lane Detection and Reconstructionmentioning

confidence: 99%

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Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Chuttur,

Kaudeerally,

Nazurally

2023

JTEC

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Lane detection is a crucial task that involves identifying lane lines and proper markings on road structures. The field of lane detection has gained significant attention due to the increasing use of autonomous vehicles and ADAS systems, as they require robust lane detection systems to navigate safely in complex environments. While previous studies have focused on lane detection in various scenarios, lane line reconstruction for dynamic road networks has been largely overlooked. To address this gap, we propose a novel approach to reconstruct lane lines for dynamic road networks using roadblocks, specifically red and white striped markers, as cues. To evaluate the effectiveness of our proposed approach, we first built and labeled our own dataset by extracting 1854 frames from recorded videos. We trained a Faster-RCNN V2 model on 80% of the dataset to detect the roadblocks and used the OpenCV DNN module, along with a range of techniques, such as ROI selection, color selection, region masking, and polynomial and linear fittings for lane detection and reconstruction. Our approach successfully reconstructed the left, middle, and right lane lines, achieving an overall accuracy of 68%, a recall of 73%, and a precision score of 73% on an unseen set of 50 images. Our study contributes to the field by proposing a novel approach for reconstructing lane lines in dynamic road networks using roadblocks as markers. This approach has the potential to enhance the accuracy and robustness of lane detection systems in complex environments. Furthermore, our work addresses a gap in the existing literature and provides insights for future research in this area.

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Section: Amentioning

confidence: 99%

Section: Amentioning

confidence: 99%

Section: Lane Detection and Reconstructionmentioning

confidence: 99%

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Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Chuttur,

Kaudeerally,

Nazurally

2023

JTEC

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“…Penelitian "A Method of Lane Edge Detection Based on Canny Algorithm" [7]. Penelitian "Real-Time Lane Detection-Based Line Segment Detection" [8]. Penelitian "Vision Based Vehicle Monitoring at Road Intersections" [9].…”

Section: Pendahuluanmentioning

confidence: 99%

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Salkiawati

Alexander

Lubis

2021

mib

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Based on the traffic accident report, it was found that there were 41,771 (Forty-one thousand seven hundred and seventy-one) incidents caused by disorderly drivers. (POLRI, 2018). One of these disorders is by driving a motorized vehicle outside the traffic lane. In this study, researchers developed computer vision using sensor methods and image processing. The stages in computer vision are the image acquisition process, the image segmentation process, and the image understanding process. This study aims to develop an application using computer vision to warn drivers of disorderly traffic or to increase the alertness of motorized vehicle drivers by detecting the condition of the driver's path. It is hoped that this research will provide a sense of security for motorized vehicle drivers, as well as provide applications that are expected to increase driver awareness to avoid traffic accidents

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Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented Sliding Window Techniques

Bhupathi

Ferdowsi

2022

Int. J. ITS Res.

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Real-Time Lane Detection-Based Line Segment Detection

Cited by 12 publications

References 7 publications

Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented Sliding Window Techniques

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