Drowsiness Detection System: Integrating YOLOv5 Object Detection with Arduino Hardware for Real-Time Monitoring
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2024, Vol 12, Issue 2
Abstract
Drowsy driving remains a significant cause of accidents worldwide, prompting the need for effective real-time monitoring systems to detect and prevent driver fatigue. In this paper, we propose a novel approach for drowsiness detection leveraging state-of-the-art deep learning techniques and compact hardware implementation. Our system integrates the YOLOv5 object detection model with Arduino hardware, offering a portable and efficient solution for on-road application. The YOLOv5 model is employed for its superior speed and accuracy in detecting facial landmarks and identifying signs of drowsiness in real-time video streams. By focusing on the key features indicative of drowsiness, such as eye closure, head nodding, and yawning, our system can effectively discern driver fatigue levels with high precision. Furthermore, the utilization of Arduino hardware enables seamless integration of the detection system into vehicles, providing a cost-effective and accessible solution for widespread deployment. Leveraging the computational capabilities of Arduino, we optimize the inference process of YOLOv5 to ensure real-time performance on resource-constrained platforms. We present experimental results demonstrating the efficacy and efficiency of our proposed drowsiness detection system. Through rigorous testing in simulated driving conditions and real-world scenarios, we validate the system's ability to accurately identify drowsiness cues while maintaining low latency. Overall, our research contributes to advancing the field of driver safety technology by offering a practical and scalable solution for drowsiness detection. The integration of YOLOv5 with Arduino hardware showcases the potential for deploying sophisticated deep learning models in real-world applications, paving the way for enhanced road safety and accident prevention measures.
Authors and Affiliations
Prof. Balwante S S, Rameshwar Kolhe, Nikhil K Pingale, and Dipendra Singh Chandel
Keywords
Related Articles
- EP ID EP745000
- DOI 10.55524/ijircst.2024.12.2.9
- Views 20
- Downloads 0
Performance on Stabilization of Soils Using Geosynthetics
A Large variety of reinforcing materials emerged and have been developed for construction purposes, including: Metal strips, bar mats, Geotextile sheets, Geo Grids, and other reinforcing materials have emerged and been...
Experimental Study on the Behavior of Concrete Using Translucent Material
Energy efficient and safety of structures have gained worldwide attention in present scenario. Concrete being considered as one of the major backbone of structures can be modified in order to improve its everlasting prop...
Comparisons of Center of Mass during Golf Swings of 7 Iron or Driver
Golf game is competitive and it has attracted many participants due to its unique features. In the many features, kinetics of golf swing was analyzed to improve one’s performance level. The present study examined the cen...
Comparison of Color Classification Using Computer Vision and Deep Neural Network
Research on artificial intelligence and machine learning is currently ongoing and is focused on real-world problems. Machine learning is used by computers to make predictions based on the provided data set or existing kn...
Reinforcement Learning: A Comprehensive Overview
Machine Learning is one of the most essential parts of Artificial Intelligence. Machine learning now exists as an important innovation and has a sufficient number of uses. Reinforcement Learning is one of the largest Mac...