An approach to classify distraction driver detection system by using mining techniques

Reddy Shiva Shankar, Pilli Neelima, Voosala Priyadarshini, Swaroop Ravi Chigurupati


According to the motor vehicle safety division, over the past 5-10 years, usage of motor vehicles has rapidly increased, in that specifical usage of cars has grown tremendously. The major contribution of this paper is a systematic evaluation of the scholarly literature on driver distraction detection techniques. Our driver distraction detection framework offers a systematic overview of evaluated methodologies for detecting driver attention. So, we need to develop a model that classifies each driver's behaviour and determines its corresponding class name. To overcome this dispute, we have attained an appreciable number of deep learning algorithms on the dataset like convolutional neural network (CNN) and VGG16 to detect what the driver is doing in the car as given in the driver images. This process can be done by predicting the likelihood of the driver's actions in each picture. Of all models, we distinguished that the VGG16 Algorithm has conquered CNN with a loss of 0.298 and an Accuracy of 91.7%.


Artificial intelligence; Computer vision; Convolutional neural network; Distraction driver detection; Machine learning;

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