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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-03988-x