Advanced Driver Drowsiness Detection: Integrating CNN and ANN Technologies for Proactive Road Safety
Keywords:
Convolutional Neural network, Artificial Neural Network, user behaviorAbstract
This paper delves into the critical realm of automated driver drowsiness identification, presenting a pivotal stride in advancing road safety through preemptive driver alerts. Employing an auto camera system, real-time images of the driver are captured, and a neural network, encompassing both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) technologies, analyzes each frame independently. The temporal dimension is introduced by averaging characteristics from the last 20 frames, aligning with approximately one second in both training and testing datasets. The research critically examines image segmentation methods, anchoring a robust model in CNN technology. With a meticulously curated dataset of over 2000 annotated image slices, the study pioneers an innovative approach to pre-emptive drowsy driving interventions, seamlessly integrating ANN and CNN analyses, thereby promising tangible contributions to road safety efforts.
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