Automatic lane marking prediction using convolutional neural network and S-Shaped Binary Butterfly Optimization

Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the intricate features present in the lane images, traditional computer vision (CV) techniques are typically time-consuming, need more computing res...

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Published in:	The Journal of supercomputing Vol. 78; no. 3; pp. 3715 - 3745
Main Authors:	Alajlan, Abrar Mohammed, Almasri, Marwah Mohammad
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2022 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial neural networks Classification Compilers Computer Science Computer vision Interpreters Marking Mathematical models Neural networks Optimization Parameters Performance evaluation Processor Architectures Programming Languages Hyperparameter optimization Lane detection Binary optimization Autonomous cars Convolutional neural network
ISSN:	0920-8542, 1573-0484
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Abstract	Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the intricate features present in the lane images, traditional computer vision (CV) techniques are typically time-consuming, need more computing resources, and use complex algorithms. To address this problem, this paper presents a deep convolutional neural network (CNN) architecture that prevents the complexities of traditional CV techniques. CNN is regarded as a reasonable method for lane marking prediction, while improved performance requires hyperparameter tuning. To enhance the initial parameter setting of the CNN, an S-Shaped Binary Butterfly Optimization Algorithm (SBBOA) is utilized in this paper. In this way, the relative parameters of CNN are selected for accurate lane marking. To evaluate the performance of the proposed SBBOA-CNN model, extensive experiments are conducted using the TUSimple and CULane datasets. The experimental results obtained show that the proposed approach outperforms other state-of-the-art techniques in terms of classification accuracy, precision, F 1-score, and recall. The proposed model also considerably outperforms the CNN in terms of classification accuracy, average elapsed time, and receiver operating characteristics curve measure. This result demonstrates that the SBBOA optimized CNN exhibits higher robustness and stability than CNN.
AbstractList	Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the intricate features present in the lane images, traditional computer vision (CV) techniques are typically time-consuming, need more computing resources, and use complex algorithms. To address this problem, this paper presents a deep convolutional neural network (CNN) architecture that prevents the complexities of traditional CV techniques. CNN is regarded as a reasonable method for lane marking prediction, while improved performance requires hyperparameter tuning. To enhance the initial parameter setting of the CNN, an S-Shaped Binary Butterfly Optimization Algorithm (SBBOA) is utilized in this paper. In this way, the relative parameters of CNN are selected for accurate lane marking. To evaluate the performance of the proposed SBBOA-CNN model, extensive experiments are conducted using the TUSimple and CULane datasets. The experimental results obtained show that the proposed approach outperforms other state-of-the-art techniques in terms of classification accuracy, precision, F 1-score, and recall. The proposed model also considerably outperforms the CNN in terms of classification accuracy, average elapsed time, and receiver operating characteristics curve measure. This result demonstrates that the SBBOA optimized CNN exhibits higher robustness and stability than CNN. Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the intricate features present in the lane images, traditional computer vision (CV) techniques are typically time-consuming, need more computing resources, and use complex algorithms. To address this problem, this paper presents a deep convolutional neural network (CNN) architecture that prevents the complexities of traditional CV techniques. CNN is regarded as a reasonable method for lane marking prediction, while improved performance requires hyperparameter tuning. To enhance the initial parameter setting of the CNN, an S-Shaped Binary Butterfly Optimization Algorithm (SBBOA) is utilized in this paper. In this way, the relative parameters of CNN are selected for accurate lane marking. To evaluate the performance of the proposed SBBOA-CNN model, extensive experiments are conducted using the TUSimple and CULane datasets. The experimental results obtained show that the proposed approach outperforms other state-of-the-art techniques in terms of classification accuracy, precision, F1-score, and recall. The proposed model also considerably outperforms the CNN in terms of classification accuracy, average elapsed time, and receiver operating characteristics curve measure. This result demonstrates that the SBBOA optimized CNN exhibits higher robustness and stability than CNN.
Author	Alajlan, Abrar Mohammed Almasri, Marwah Mohammad
Author_xml	– sequence: 1 givenname: Abrar Mohammed surname: Alajlan fullname: Alajlan, Abrar Mohammed email: aalajlan1@ksu.edu.sa organization: Self-Development Skills Department, Common First Year Deanship, King Saud University – sequence: 2 givenname: Marwah Mohammad surname: Almasri fullname: Almasri, Marwah Mohammad organization: College of Computing and Informatics, Saudi Electronic University
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Cites_doi	10.1016/j.eswa.2017.11.028 10.1007/s00500-018-3102-4 10.1016/j.cose.2018.04.009 10.1109/TVT.2019.2949603 10.1016/j.eswa.2018.12.005 10.1504/IJBET.2019.103242 10.1007/s10115-018-1263-1 10.1080/01441647.2018.1494640 10.1109/ACCESS.2020.3019245 10.1007/s00500-018-3124-y 10.1016/j.advengsoft.2015.01.010 10.1016/j.amc.2019.124919 10.1177/0361198120922210 10.1016/j.chemolab.2015.08.020 10.1007/s11042-019-7577-5 10.3390/s19071665 10.1016/j.knosys.2020.105584 10.1007/s00521-020-05474-6 10.1109/ICETETS.2016.7603013 10.1002/pip.3315 10.1007/s13369-020-04918-4 10.1002/ett.4132 10.1007/s11042-021-11123-4 10.1109/ICCV.2017.215 10.1016/j.engappai.2020.104079 10.1007/978-3-319-93025-1_4 10.1155/2018/8320207 10.1109/TITS.2021.3088488 10.1007/s12652-020-02424-x 10.1002/rob.22020 10.1007/s11042-020-10248-2 10.1016/j.dib.2019.105046 10.1109/CVPR46437.2021.00036 10.1109/ICCV.2019.00301 10.1109/TIE.2021.3066943 10.1016/j.isatra.2020.10.052 10.1016/j.matpr.2020.09.605 10.1007/s40747-021-00422-w 10.1016/j.compeleceng.2020.106653 10.1016/j.isprsjprs.2020.05.022 10.1016/j.renene.2020.08.125 10.1109/ICCSP.2019.8698065 10.1016/j.jvcir.2019.102675 10.1109/CVPR.2019.01185
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Snippet	Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the...
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SubjectTerms	Accuracy Algorithms Artificial neural networks Classification Compilers Computer Science Computer vision Interpreters Marking Mathematical models Neural networks Optimization Parameters Performance evaluation Processor Architectures Programming Languages
Title	Automatic lane marking prediction using convolutional neural network and S-Shaped Binary Butterfly Optimization
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