**Advanced Driver Fatigue Detection by Integration of OpenCV DNN Module and Deep Learning †**

**Muzammil Parvez M. 1,\*, Srinivas Allanki 1,\*, Govindaswamy Sudhagar 2, Ernest Ravindran R. S. 1, Chella Santosh 1, Ali Baig Mohammed <sup>3</sup> and Mohd. Abdul Muqeet <sup>4</sup>**


**Abstract:** Road safety is significantly impacted by drowsiness or weariness, which primarily contributes to auto accidents. If drowsy drivers are informed in advance, many fatal incidents can be avoided. Over the past 20 to 30 years, the number of road accidents and injuries in India has increased alarmingly. According to the experts, the main cause of this issue is that drivers who do not take frequent rests when travelling long distances run a great danger of becoming drowsy, which they frequently fail to identify early enough. Several drowsiness detection techniques track a driver's level of tiredness while they are operating a vehicle and alert them if they are not paying attention to the road. This study describes a noncontact way of determining a driver's tiredness utilising detecting techniques.

**Keywords:** OpenCV; DNN module; face detection; CNN
