• Muppala Naga Keerthi
• komara neelakantam

Driver drowsiness detection is crucial for road safety, as drowsy driving is a leading cause of accidents. This research presents a machine learning-based approach for detecting driver drowsiness using Opencv and deep learning and Keras frameworks. The system employs a camera to capture real-time video footage of the driver’s face. Key features such as eye closure and mouth movements are extracted through preprocessing techniques using Opencv and deep learning. These features are then used to train a Convolutional Neural Network (CNN) model with a large labeled dataset of video frames depicting both alert and drowsy drivers. Once trained, the CNN model predicts the driver’s drowsiness level in real-time, helping to identify signs of fatigue early. In addition to real-time alerts, the system provides a graphical representation of the drowsiness score over time, enabling continuous monitoring of the driver’s fatigue levels. This feature allows for proactive intervention, ensuring safer driving conditions. The proposed system utilizes deep learning techniques, particularly CNNs, which have proven effective in recognizing facial features and behaviours associated with drowsiness. The integration of real-time monitoring and visual feedback enhances the system’s accuracy and response time. By leveraging such advanced technology, this approach has the potential to significantly reduce traffic accidents caused by drowsy driving, offering a valuable tool for improving road safety and preventing accidents related to driver fatigue.

Driver drowsiness detection, CNN, Opencv and deep learning, Keras, machine learning, deep learning, driver fatigue, real-time monitoring, traffic safety, video processing, eye closure detection, driver alertness

"Neural Nap Guard: CNN-Driven Drowsiness Detection Using Opencv and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.h376-h383, July-2025, Available :http://www.jetir.org/papers/JETIR2507749.pdf

"Neural Nap Guard: CNN-Driven Drowsiness Detection Using Opencv and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pph376-h383, July-2025, Available at : http://www.jetir.org/papers/JETIR2507749.pdf