Detecting Safe and Not Safe Driving Actions using Convolutional Neural Network
DOI:
https://doi.org/10.32628/CSEIT217287Keywords:
Distracted Driver Detection, InceptionV3, VGG16, Resnet50, Image Classification, Computer Vision.Abstract
The main cause of accidents is due to Manual, Visual or Cognitive distraction out of these three Manual distractions are concerned with various activities where “driver’s hands are off the wheel”. Such distractions include talking or texting using mobile phones, eating and drinking, talking to passengers in the vehicle, adjusting the radio, makeup, etc. To solve the problem of manual distraction, the Convolutional Neural Network (CNN) model of ResNet-50 using transfer learning with 23,587,712 parameters was used. The dataset used is from State Farm Distracted Driver Detection Dataset. The training accuracy is 97.27% and validation accuracy is 55%. Further the model works on detecting real-time distractions on a video feed for this purpose the system uses OpenCV and the model is integrated with the frontend using the flask.
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