Abstract: Distracted driving is any activity that deviates an individual’s attention from driving. Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver fatigue. Research efforts have been made to monitor drivers' attention states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver's attention states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. The existing work of distracted driver detection is concerned with a limited set of distractions(Mainly cell phone usage).In this paper, a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning based Faster Region Convolution Neural Network (FRCNN).This project uses the state farm distracted driver detection, which contains four classes: calling, texting, looking behind, and normal driving The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our data set. The class activation map (CAM) of our proposed method is subjectively more reasonable, which would enhance the reliability and explain ability of the model.

Keywords: Alert Message, DD, FRCNN, Face Detection,

PDF | DOI: 10.17148/IJARCCE.2023.12563

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