Abstract
In recent years, the number of motor vehicle traffic crashes is rising around the world. According to the United States National Highway Traffic Safety Administration (NHTSA), the main cause of road fatalities and injuries is distracted drivers. Driver distraction is a specific sort of driver interference on the roadway. In this case, a deep learning-based system can detect and distinguish the source of distractions in real-time, to avoid traffic accidents and make better transport safety. This paper attempts to develop the system using transfer learning and fine-tuning methods with different model architectures. Various pre-trained weights were used with fine-tuning to improve accuracy and implemented using Mobile Net, VGG16, and ResNet50 models. Finally, the results illustrate that transfer learning on Mobile Net with frozen layers is the best model out of three models getting 99% (495 out of 500) of accuracy on the test dataset images.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
