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 | Intelligent Automatic Driver Distraction Detection and Classification Using CNN Deep Learning Model in Vehicles |
|---|---|
| ABSTRACT | currently, one of the main factors thought to be responsible for traffic accidents are distraction of driver. Intelligent automobile driving technologies are therefore becoming increasingly crucial. It is clear that driver-assistance systems, which recognize drivers' behaviors and aid them in driving safely, have become more and more popular in recent years. Although there are other forms of data as well, such as the driver's physical conditions, aural and visual features, and automobile information, the main source of data for these research is a camera in the car that captured pictures of the driver's hands, arms, and face. This study proposes a convolution neural network (CNN)-based architecture for the classification and detection of driver distraction. A novel architecture based on the EfficientNetV2 architecture was introduced to run heavy convolutional networks for large-scale image recognition. It makes use of an efficient CNN with excellent accuracy. The State Farm dataset was used to assess the proposed architecture for detecting driver attention. This kind of study often uses this data, which may be accessible publicly on Kaggle. The recommended architecture has a 99.4% accuracy rate. |
| AUTHOR | ADITI GHOLAP, AADESH RAWAS, RUTUJA KURHADE, SWAPNIL BANGAL UG Student, Dept. of Electronic & Computer Engineering, PREC, Loni, Maharashtra, India Assistant Professor, Dept. of Electronic &Computer Engineering, PREC, Loni, Maharashtra, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405109 |
| pdf/109_Intelligent Automatic Driver Distraction Detection and Classification Using CNN Deep Learning Model in Vehicles.pdf | |
| KEYWORDS | |
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