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• W4313334502 abstract "Distracting while driving is a serious issue that causes serious direct and indirect harm to the society. To avoid these problems, detecting dangerous drivers’ behaviour is very important.This research focuses on detecting driver behaviour with a combination of artificial deep learning and machine learning models with genetic algorithm. Most of the previous works have focused on using convolutional neural network as deep learning model or support vector machine as machine learning model for actions detection of drivers from input images. The proposed structure will use genetic algorithms to first choose the structure of feature extractor from famous CNN models such as VGG19, ResNet50, and DenseNet121. After mentioning feature extractor, proposed framework contains two layer of dense layer for classification as a deep learning model. On the machine learning side K nearest neighbor, random forest, support vector machine, and extreme boost algorithms have been used as classifiers. Genetic algorithms will specify number of neurons and activation functions of these layers for deep learning and hyperparameters such as number of estimators for machine learning models. Proposed model has been developed with the use of state farm dataset that contains information of 1 safe driving class and 9 dangerous behaviours such as texting while driving, talking with passengers, drinking, etc. Experimental results indicate 99.80% accuracy for classification of the state farm distracted driver detection with combination of genetic algorithms and deep neural networks. Compared to similar research, the proposed approach has shown superior results for classification of state farm distracted driver detection. Proposed approach chooses the feature extraction model and hyperparameters of the classification layer automatically. Thus it can be used for driving behaviour classification with seeing new situation too. Proposed framework can be used as a real time driver’s distraction detection to decrease car traffic accidents and alleviate corresponding damages to the drivers." @default.
• W4313334502 created "2023-01-06" @default.
• W4313334502 creator A5041611314 @default.
• W4313334502 date "2023-03-01" @default.
• W4313334502 modified "2023-10-04" @default.
• W4313334502 title "Real-time driver distraction recognition: A hybrid genetic deep network based approach" @default.
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• W4313334502 doi "https://doi.org/10.1016/j.aej.2022.12.009" @default.
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