Convolutional neural network-based driver drowsiness detection system

Jampala Rishi Krishna; N. Anusha; G. Karthik; Eega Rama Krishna; Gajerla Roshith

Author(s)

Jampala Rishi Krishna Department of Computer Science and Engineering (Data Science), Vidya Jyothi Institute of Technology,Hyderabad, Telangana,India

N. Anusha Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Vidya Jyothi Institute of Technology,Hyderabad, Telangana,India

G. Karthik Department of Computer Science and Engineering (Data Science), Vidya Jyothi Institute of Technology,Hyderabad, Telangana,India

Eega Rama Krishna Department of Computer Science and Engineering (Data Science), Vidya Jyothi Institute of Technology,Hyderabad, Telangana,India

Gajerla Roshith Department of Computer Science and Engineering (Data Science), Vidya Jyothi Institute of Technology,Hyderabad, Telangana,India

Abstract

Driver drowsiness and fatigue form the two main causes for road accidents around the globe, affecting more people who fall in the age group 18-45. In this paper, a proposed Driver Safety System (DSS) is aimed at detecting realtime drivers' signs of fatigue. The system captures video at all times of the face of the driver and develops each frame into grayscale images using the HAAR CASCADE algorithm, which is a very reliable object detection tool. MobileNet takes these images to deep recognition and tracking of closed eyes per frame. Inside the decision block, a counter logs the period over which the eyes close that puts a flag on the drowsy driver, raises an alert, and then restarts the counter. For higher accuracy, the DSS integrates other Convolutional Neural Networks (CNN) models that are implemented, such as ResNet50, MobileNetV2, and VGG16. Using pre-trained layers, it enhances the system to more accurately classify distracted behavior. Optimized as a High Precision Low Power (HPLP) prototype, it functions at its best under consistent lighting with a homogeneous background, so there are no reflections or interference from the background. Testing has shown that this approach, based on CNN, significantly outperforms existing techniques with better accuracy. The DSS would process video in real-time and identify fatigue much in advance, thus proving its meaningful contribution to safer driving through timely alert mechanisms.

Publication Details

Page(s) 540-547

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 20, Issue: 8, Year: 2025

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Keywords

Image Processing, Transfer Learning, Face Detection, MobileNet, drowsiness, haar cascade classifier

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