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- W3201640557 abstract "The issue of safe driving is one that affects people all over the globe. A large number of fatal accidents occur. Driving a car is a difficult task that requires comprehensive concentration and concentration. Distortions can be classified into three categories: visual diversions (driver's eyes are taken off the road), manual distractions ( driver's hands are taken off the wheel), and cognitive distractions (driver's mind is taken off the driving task). A total of 36,750 people died in motor vehicle crashes in 2018, according to the National Highway Traffic Safety Administration (NHTSA). Our methodology automatically detects and notifies the car owners when they are engaging in disoriented driving behaviour. A Real-Life Drowsiness Dataset created by a research team at the University of Texas at Arlington was used to detect multi-stage sleepiness. We used the StateFarm dataset, which contained snapshots taken from a video captured by a camera mounted in the car, to create our visualisation.In the case of a classification algorithm where the forecasting input is a likelihood value in the range of 0 to 1, accuracy and logarithmic loss (also known as cross-entropy) is used to quantify the effectiveness of the system. Each layer serves a specific function: e.g., Average pooling on a global scale, or Layers with dropouts Layers of batch normalisation and densityWith the weights from training on the ImageNet dataset, we used classification models and CNN, LSTM and VGG -16 and VGG-16, RESNET 50, Xception and MobileNet models used for drowsiness and distraction datasets respectively. We obtained good results from all few of the architectures and their accuracies are shown in Fig.1. Fig.1. Accuracy of different algorithms per datasetThis problem will be solved by developing a recognition system to recognize key characteristics of drowsiness and distraction, as well as sending out a warning when one becomes drowsy before it is too late." @default.
- W3201640557 created "2021-09-27" @default.
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- W3201640557 date "2021-09-16" @default.
- W3201640557 modified "2023-09-23" @default.
- W3201640557 title "Deep Learning Models used to study the driver behaviour with alert system" @default.
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