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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Alerting Distracted Driver Using Cnn |
| Authors Name: | Amritha lal , Aksa Saji , Abhishek S , Abhishek S , Liji Samuel |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_181301
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| Published Paper Id: | IJNRD2205078 |
| Published In: | Volume 7 Issue 5, May-2022 |
| DOI: | |
| Abstract: | Driver distraction is a leading factor in n crashes. To reduce vehicle accidents and improve transportation safety, a system that can classify distracted driving is highly desirable and has attracted much research interest in recent years. With a goal to reduce traffic accidents and improve transportation safety, this project proposes a driver distraction detection and alerting system which identifies various types of distractions through a camera by observing the driver and alerts the driver by buzzer . In deep learning, a Convolutional neural network is a class of deep neural networks, most commonly applied to analysing visual imagery and our goal is to build a high-accuracy model to distinguish whether driver is driving safely or conducting a particular kind of distraction activity. a multi-layer CNN network is constructed in the model and the key parameters of the input layer, convolution layer, pooling layer, fully connected layer and output layer are optimized as well. The results of experimental analysis show that the accuracy of the proposed method can reach 97.31%, which is higher than that of the existing machine learning algorithms. Therefore, the proposed method is efective in improving the accuracy of distracted driving recognition The input of our model are images of driver taken in the car and the model is trained with the dataset created by ourselves. If the driver distracts from driving it will be classified as distracted and develops an alert which reminds the driver to focus on the driving task when he/she gets distracted. In general, most of the existing systems are not fit for real applications as they are wearable, induce discomfort and take long time to make a decision. |
| Keywords: | Convolutional neural networks, data augmentation techniques, deep learning methods, distracted driver ,alerting system . |
| Cite Article: | "Alerting Distracted Driver Using Cnn", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 5, page no.710-722, May-2022, Available :http://www.ijnrd.org/papers/IJNRD2205078.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2205078 Registration ID: 181301 Published In: Volume 7 Issue 5, May-2022 DOI (Digital Object Identifier): Page No: 710-722 Country: KOLLAM , Kerala , India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2205078 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2205078 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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