Abstract: With the intervention of social media and internet technology, people are getting more and more careless and distracted while driving which is having a severe detrimental effect on the safety of the driver and his fellow passengers. To provide an effective solution, this paper put forwards a Machine Learning model using Convolutional Neural Networks to not only detect the distracted driver but also identify the cause of his distraction by analyzing the images obtained using the camera module installed inside the vehicle. Convolutional neural networks are known to learn spatial features from images, which can be further examined by fully connected neural networks. The experimental results show a 99% average accuracy in distraction recognition and hence strongly support that our Convolutional neural networks model can be used to identify distraction among the drivers.
Conclusion: Deep learning using Convolutional Neural Networks  has become a hot area in Machine Learning research, and it has been extensively used in image classification, voice recognition, etc. In this paper, we use Deep Convolutional Networks for detecting distracted drivers and also identifying the cause of their distraction using the VGG16  and VGG19  model. The above results suggest that the methods discussed in this work can be used to develop a system using which distraction while driving can be detected among drivers. The model proposed can automatically identify any of the mentioned 10 classes of distraction and identify not only basic distraction but also their cause of distraction. With an accuracy of more than 99%, the mentioned system was shown to be efficient and workable. The proposed system can be a part of some Driver State Monitoring System which will effectively monitor the state of the driver while he is driving. Driver state monitoring has been becoming increasingly popular these days and many automobile giants have started adopting such systems as a methodology to prevent accidents. These systems, when installed inside vehicles will raise warnings whenever the driver gets distracted, thus trying to prevent any accidents due to distraction from the driver. Also in this work a significant amount of training time has been shown to be reduced. When pre-trained weights from ImageNet  were not used, the training time increased by around 50 times for both VGG16  and VGG19 . A graphical representation of time elapsed is depicted in Fig. 15. This drastic reduction in training time was achieved without diminishing the accuracy of our classification models. In future work as an extension to this work, more categories of distraction can be brought in. Even considering certain specific scenarios, which were not targeted in the present work, such as detecting drowsiness among drivers may also provide an opportunity to widen the scale of the work and build a more efficient system.