Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis

Rasheed Raed; Abu Hadrous Iyad

doi:doi:10.11648/j.ijiis.20221104.12

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Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis

Rasheed Raed, Abu Hadrous Iyad

Published in International Journal of Intelligent Information Systems (Volume 11, Issue 4)

Received: 18 August 2022 Accepted: 13 September 2022 Published: 11 October 2022

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Abstract

Human mistake is virtually always to blame in situations involving motor vehicles, which can have fatal consequences. When it comes to lane detection, the analysis of driving scenarios that is carried out by dashboard cameras that are placed in vehicles as part of advanced driver assistance systems (ADAS) is of the utmost significance. The initial developments in lane detection systems resulted in the creation of two distinct varieties. Image processing and deep segmentation have typically relied on a number of different methods. The techniques of deep segmentation are not yet capable of resolving many of the most important and challenging issues. We came up with a solution to the problem of object lanes overlapping each other and developed a dependable technique for lane detection that can be used in driving scene analysis systems. The method that is provided for real-time object detection makes use of the real-time object detection algorithms that are the most up-to-date and effective currently available; these algorithms are collectively referred to as YOLOv5. By identifying the object-lane that is overlapping the lane that has been unequivocally found by removing items that have overlapped, it is possible to solve this problem.

Published in	International Journal of Intelligent Information Systems (Volume 11, Issue 4)
DOI	10.11648/j.ijiis.20221104.12
Page(s)	65-69
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

ADAS, Lane Detection, Object Detection, YOLOv5, Sobel Filter

References

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[2]	Y. T. E. S. D. Wang, " Lane detection and tracking using b-snake," Image and Vision Computing, 2004.
[3]	B. W. T. T. S. K. J. S. W. P. J. A. M. P. M. T. C.-Y. R. e. a. Huval, "An empirical evaluation of deep learning on highway driving," arXiv preprint arXiv:, 2015.
[4]	H. W. a. X. L. Zequn Qin, "Ultra Fast Structure-aware Deep Lane Detection," arXiv, 2020.
[5]	H. C. e. al., "Pre-Trained Image Processing Transformer," IEEE Xplore, 2021.
[6]	Z. B. a. P. Cao, "Color space conversion algorithm and comparison study," Journal of Physics: Conference Series, 2021.
[7]	V. S. a. G. P. Akshita Akasapu, "Implementation of Sobel filter using CUDA," IOP Conf. Series: Materials Science and Engineering, 2022.
[8]	D. Thuan, "Evolution Of Yolo Algorithm And Yolov5: The State-Of-The-Art Object Detection Algorithm," Bachelor’s Thesis - Information Technology Oulu University of Applied Sciences, 2021.
[9]	K.-Y. a. S.-F. L. Chiu, "Lane detection using color-based segmentation.," IEEE Proceedings. Intelligent Vehicles Symposium, 2005.
[10]	H.-Y. B.-S. J. P.-T. T. a. K.-C. F. Cheng, "Lane detection with moving vehicles in the traffic scenes," IEEE Transactions on intelligent transportation systems, 2006.
[11]	C. a. J.-H. M. Lee, "Robust lane detection and tracking for real-time applications.," IEEE Transactions on Intelligent Transportation Systems, 2018.
[12]	A. J. S. C. C. F. L. a. T. G. López, "Robust lane markings detection and road geometry computation," International Journal of Automotive Technology, 2010.
[13]	A. A. B. a. G. L. Mammeri, "meri, Abdelhamid, Azzedine Boukerche, and Guangqian Lu." Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter," Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, 2014.
[14]	Y. L. R. Q. a. Z. Y. Muhammad Monjurul Karim, "A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and prevention," arXiv, 2021.
[15]	W. X. Y. C. F. L. M. L. V. M. a. T. D. Fisher Yu, "A diverse driving video database with scalable annotation tooling," arXiv, 2018.

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APA Style

Rasheed Raed, Abu Hadrous Iyad. (2022). Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. International Journal of Intelligent Information Systems, 11(4), 65-69. https://doi.org/10.11648/j.ijiis.20221104.12

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ACS Style

Rasheed Raed; Abu Hadrous Iyad. Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. Int. J. Intell. Inf. Syst. 2022, 11(4), 65-69. doi: 10.11648/j.ijiis.20221104.12

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AMA Style

Rasheed Raed, Abu Hadrous Iyad. Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. Int J Intell Inf Syst. 2022;11(4):65-69. doi: 10.11648/j.ijiis.20221104.12

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@article{10.11648/j.ijiis.20221104.12,
  author = {Rasheed Raed and Abu Hadrous Iyad},
  title = {Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {4},
  pages = {65-69},
  doi = {10.11648/j.ijiis.20221104.12},
  url = {https://doi.org/10.11648/j.ijiis.20221104.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221104.12},
  abstract = {Human mistake is virtually always to blame in situations involving motor vehicles, which can have fatal consequences. When it comes to lane detection, the analysis of driving scenarios that is carried out by dashboard cameras that are placed in vehicles as part of advanced driver assistance systems (ADAS) is of the utmost significance. The initial developments in lane detection systems resulted in the creation of two distinct varieties. Image processing and deep segmentation have typically relied on a number of different methods. The techniques of deep segmentation are not yet capable of resolving many of the most important and challenging issues. We came up with a solution to the problem of object lanes overlapping each other and developed a dependable technique for lane detection that can be used in driving scene analysis systems. The method that is provided for real-time object detection makes use of the real-time object detection algorithms that are the most up-to-date and effective currently available; these algorithms are collectively referred to as YOLOv5. By identifying the object-lane that is overlapping the lane that has been unequivocally found by removing items that have overlapped, it is possible to solve this problem.},
 year = {2022}
}

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TY  - JOUR
T1  - Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis
AU  - Rasheed Raed
AU  - Abu Hadrous Iyad
Y1  - 2022/10/11
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221104.12
DO  - 10.11648/j.ijiis.20221104.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 65
EP  - 69
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221104.12
AB  - Human mistake is virtually always to blame in situations involving motor vehicles, which can have fatal consequences. When it comes to lane detection, the analysis of driving scenarios that is carried out by dashboard cameras that are placed in vehicles as part of advanced driver assistance systems (ADAS) is of the utmost significance. The initial developments in lane detection systems resulted in the creation of two distinct varieties. Image processing and deep segmentation have typically relied on a number of different methods. The techniques of deep segmentation are not yet capable of resolving many of the most important and challenging issues. We came up with a solution to the problem of object lanes overlapping each other and developed a dependable technique for lane detection that can be used in driving scene analysis systems. The method that is provided for real-time object detection makes use of the real-time object detection algorithms that are the most up-to-date and effective currently available; these algorithms are collectively referred to as YOLOv5. By identifying the object-lane that is overlapping the lane that has been unequivocally found by removing items that have overlapped, it is possible to solve this problem.
VL  - 11
IS  - 4
ER  -

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Author Information

Rasheed Raed

Faculty of Engineering, Islamic University of Gaza, Gaza, Palestine
Abu Hadrous Iyad

Faculty of Engineering, Islamic University of Gaza, Gaza, Palestine

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APA Style

Rasheed Raed, Abu Hadrous Iyad. (2022). Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. International Journal of Intelligent Information Systems, 11(4), 65-69. https://doi.org/10.11648/j.ijiis.20221104.12

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ACS Style

Rasheed Raed; Abu Hadrous Iyad. Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. Int. J. Intell. Inf. Syst. 2022, 11(4), 65-69. doi: 10.11648/j.ijiis.20221104.12

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AMA Style

Rasheed Raed, Abu Hadrous Iyad. Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis. Int J Intell Inf Syst. 2022;11(4):65-69. doi: 10.11648/j.ijiis.20221104.12

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@article{10.11648/j.ijiis.20221104.12,
  author = {Rasheed Raed and Abu Hadrous Iyad},
  title = {Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {4},
  pages = {65-69},
  doi = {10.11648/j.ijiis.20221104.12},
  url = {https://doi.org/10.11648/j.ijiis.20221104.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221104.12},
  abstract = {Human mistake is virtually always to blame in situations involving motor vehicles, which can have fatal consequences. When it comes to lane detection, the analysis of driving scenarios that is carried out by dashboard cameras that are placed in vehicles as part of advanced driver assistance systems (ADAS) is of the utmost significance. The initial developments in lane detection systems resulted in the creation of two distinct varieties. Image processing and deep segmentation have typically relied on a number of different methods. The techniques of deep segmentation are not yet capable of resolving many of the most important and challenging issues. We came up with a solution to the problem of object lanes overlapping each other and developed a dependable technique for lane detection that can be used in driving scene analysis systems. The method that is provided for real-time object detection makes use of the real-time object detection algorithms that are the most up-to-date and effective currently available; these algorithms are collectively referred to as YOLOv5. By identifying the object-lane that is overlapping the lane that has been unequivocally found by removing items that have overlapped, it is possible to solve this problem.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Robust Lane Detection by Removing Overlapped Objects for Complex Driving Scene Analysis
AU  - Rasheed Raed
AU  - Abu Hadrous Iyad
Y1  - 2022/10/11
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221104.12
DO  - 10.11648/j.ijiis.20221104.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 65
EP  - 69
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221104.12
AB  - Human mistake is virtually always to blame in situations involving motor vehicles, which can have fatal consequences. When it comes to lane detection, the analysis of driving scenarios that is carried out by dashboard cameras that are placed in vehicles as part of advanced driver assistance systems (ADAS) is of the utmost significance. The initial developments in lane detection systems resulted in the creation of two distinct varieties. Image processing and deep segmentation have typically relied on a number of different methods. The techniques of deep segmentation are not yet capable of resolving many of the most important and challenging issues. We came up with a solution to the problem of object lanes overlapping each other and developed a dependable technique for lane detection that can be used in driving scene analysis systems. The method that is provided for real-time object detection makes use of the real-time object detection algorithms that are the most up-to-date and effective currently available; these algorithms are collectively referred to as YOLOv5. By identifying the object-lane that is overlapping the lane that has been unequivocally found by removing items that have overlapped, it is possible to solve this problem.
VL  - 11
IS  - 4
ER  -

Copy | Download