International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Alertify - Stay Awake Stay Alive |
|---|---|
| ABSTRACT | Drowsy driving is a major contributor to road accidents worldwide, posing significant risks to driver safety and public health. Early detection of driver fatigue can greatly reduce such incidents and save lives. This research presents “Alertify – Stay Awake, Stay Alive” a hybrid system that integrates computer vision and sensor-based techniques to detect driver drowsiness in real time. The vision module employs Convolutional Neural Networks (CNNs) and the Eye Aspect Ratio (EAR) method to monitor eye closure and yawning patterns, while the MPU6050 sensor captures head movement data to enhance detection accuracy. When signs of drowsiness are identified, the system issues immediate audio and visual alerts to re-engage the driver’s attention. Experimental results demonstrate that combining visual and sensor-based features significantly improves reliability and responsiveness compared to single-mode detection systems. The proposed system offers a cost-effective and scalable solution to enhance road safety and prevent fatigue-related accidents. |
| TITLE | |
| AUTHOR | KRANTI THETE, SHRADDHA JADHAV, ANIS SHAIKH, DIA KOUL, PROF. DIKSHA GIRIGOSAVI |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311034 |
| pdf/34_Alertify - Stay Awake Stay Alive.pdf | |
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