+44 330 818 7254Abirami A, Bhuvaneswari S, Vishnuvardhan M, Yadam Karthik* and Saisuraj Shankar
The implementation of a drowsiness-driving detection system using Maixdock. Drowsy driving can be defined as a behavioral decline in driving skills. In this work, deep learning has been used to classify drowsiness symptoms such as blinking and yawning. The sample images were used to train the CNN architecture. A 4-layer convolution filter has been added as a layer in this CNN architecture. Adam optimization algorithm was then used to train the CNN. A real-time study on the effectiveness of this prototype was conducted on 10 individuals. This proposed system successfully demonstrates a classification accuracy rate between 80%. Other factors that can affect the rate of classification accuracy, such as camera distance from the driver and lighting factors, are also studied. If the driver is drunk, then the vehicle ignition will not start until the driver is not changed. In case the car is already in driving condition, then the system alerts the driver using a buzzer and pulse sensor also detects the readings and alerts the driver, if the risk is present or not. It collects information using a variety of sensors and an onboard camera. The collected data can then be uploaded to a central server.
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