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 | Multimodal Driver Drowsiness Detection Using CNN |
|---|---|
| ABSTRACT | Driver drowsiness is one of the major causes of road accidents worldwide, as fatigue significantly reduces a driver’s attention, reaction time, and decision-making ability. Early detection of drowsiness can help prevent accidents and improve road safety. This paper presents a multimodal driver drowsiness detection system that identifies fatigue indicators using deep learning techniques. The proposed system utilizes Convolutional Neural Networks (CNN) to analyze visual cues associated with driver fatigue, specifically eye closure and yawning behavior. Two separate CNN models are trained using eye and yawning image datasets to classify driver states. The eye detection model determines whether the driver’s eyes are open or closed, while the yawning detection model identifies yawning or non-yawning conditions. The system processes input images through preprocessing steps including image resizing, normalization, and data augmentation before feeding them into the CNN models for classification. The trained models are evaluated using performance metrics such as accuracy, confusion matrix, and classification report. Experimental results demonstrate that the proposed multimodal approach effectively identifies signs of driver fatigue and improves detection reliability by combining multiple visual indicators. The system can be extended for real-time driver monitoring applications, intelligent transportation systems, and vehicle safety technologies. |
| AUTHOR | Y. SAI PRASANNA, R. HEMANTH KUMAR, P. KARTHIK, P. UMA VENKATA MAHESH, R. MANASA, DR. R. SATISH Department of Electronics and Communication Engineering, Sir C. R. Reddy College of Engineering, Eluru, India Professor, Department of Electronics and Communication Engineering, Sir C. R. Reddy College of Engineering, Eluru, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404095 |
| pdf/95_Multimodal Driver Drowsiness Detection Using CNN.pdf | |
| KEYWORDS | |
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