Real-Time Binary Cell Phone Usage Detection and Classification on Vehicular Edge Devices

IoT binary classification tasks can benefit significantly from edge computing because it allows for real-time processing and decision-making. By gathering and processing data locally, edge devices can lower latency and enable quicker response times, which is beneficial for applications whose main aims are safety and security. Cell phone usage while driving is one of the worst scenarios that decreases traffic safety and causes accidents. A wide range of new applications and services could be possible with the convergence of IoT and cell phone detection in the car while in driving mode. Machine learning methods, which include the ability to track people and objects in realtime, increase public safety by identifying and preventing potential security threats and improve transportation system efficiency by streamlining traffic and easing congestion. Although object detection is the most common approach for cell phone detection, the binary classification approach has been proposed because of its fast processing ability and easy deployment on edge devices. The device, in consideration, incorporates an inside camera to gather driver image data to perform binary classification. After collecting images from edge devices, these data are prepared in detail in an IID (independently and identically distributed) manner for better training for deep learning models. After training, test results are obtained by interpolation and extrapolation analyses. Results show that interpolation accuracy increases by $1.1 \%$ and extrapolation accuracy increases by $16.5 \%$.