An Efficient Deep Learning Framework for Distracted Driver Detection

The number of road accidents has constantly been increasing recently around the world. As per the national highway traffic safety administration’s investigation, 45% of vehicle crashes are done by a distracted driver right around each. We endeavor to build a precise and robust framework for distinguishing diverted drivers. The existing work of distracted driver detection is concerned with a limited set of distractions (mainly cell phone usage). This paper uses the first publicly accessible dataset that is the state farm distracted driver detection dataset, which contains eight classes: calling, texting, everyday driving, operating on radio, inactiveness, talking to a passenger, looking behind, and drinking performed by 26 subjects to prepare our proposed model. The transfer values of the pertained model EfficientNet are used, as it is the backbone of EfficientDet. In contrast, the EfficientDet model detects the objects involved in these distracting activities and the region of interest of the body parts from the images to make predictions strong and accomplish state-of-art results. Also, in the Efficientdet model, we implement five variants: Efficientdet (D0-D4) for detection purposes and compared the best Efficientdet version with Faster R-CNN and Yolo-V3. Experimental results show that the proposed approach outperforms earlier methods in the literature and conclude that EfficientDet-D3 is the best model for detecting distracted drivers as it achieves Mean Average Precision (MAP) of 99.16% with parameter setting: learning rate of
$le-3$
, 50 epoch, batch size of 4, and step size of 250, demonstrating that it can potentially help drivers maintain safe driving habits.