CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection

The evolution of new trends in the automobile industry is creating more comfortable and convenient means of transportation. But still there exist manifold challenges in detecting legal and illegal drivers; therefore, evaluating the behaviors of drivers needs to be addressed. Taking these into consid...

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Vydané v:Signal, image and video processing Ročník 17; číslo 6; s. 3063 - 3071
Hlavní autori: Priyadharshini, G., Ukrit, M. Ferni
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.09.2023
Springer Nature B.V
Predmet:
Artificial neural networks
Authentication
Behavior
Big Data
Circulatory system
Computer Imaging
Computer Science
Datasets
Driver behavior
Energy consumption
Estimation
Fault diagnosis
Fuzzy logic
Global positioning systems
GPS
Image Processing and Computer Vision
Machine learning
Motorcycles
Multimedia Information Systems
Neural networks
Optimization
Original Paper
Pattern Recognition and Graphics
Roads & highways
Signal,Image and Speech Processing
Smartphones
Support vector machines
Vehicles
Vision
Datasets
Stacked autoencoder
Driver
Cascade region
Convolutional neural network
Authentication
ISSN:1863-1703, 1863-1711
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Shrnutí:The evolution of new trends in the automobile industry is creating more comfortable and convenient means of transportation. But still there exist manifold challenges in detecting legal and illegal drivers; therefore, evaluating the behaviors of drivers needs to be addressed. Taking these into consideration his paper proposes a novel cascade region convolutional neural network-based circulatory system optimization algorithm (CRCNN++ based CSO) to attain optimal multitask framework which includes behavior evaluation, identity authentication of drivers, vehicle diagnosis as well as estimating the fault of the vehicle. In this paper, two diverse naturalistic driving behavior public datasets namely HCRL and UAH drive datasets are collected and pre-processed via normalization as well as scaling process. The preprocessed feature is then extracted and the dimensions are minimized using the stacked autoencoder technique. The CRCNN++-based CSO is employed in determining to multitask which includes identity authentication, behavioral evaluation, vehicle diagnosis, and faults estimation is performed. Finally, the efficiency of the proposed CRCNN++-based CSO method is analyzed by evaluating various metrics namely receiver operating characteristic curve, accuracy, false positive rate, precision, Cohen Kappa score, true positive rate, and F1-Score. The comparative analysis is carried out for various existing techniques and the proposed approach. From the evaluation results, it is revealed that the proposed CRCNN++-based CSO approach delivers better performance in driver identification through driving style behavior.
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ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-023-02527-w