3d8d135818d1675848a4aef 20240521015940011 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 05 30 2024 13 6 Assistant Professor, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India. Mr. T. Nandhakumar https://orcid.org/0009-0006-6009-9876 Ms. S. Swetha https://orcid.org/0009-0005-2211-6076 UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India. Ms. T. Thrisha https://orcid.org/0009-0001-6010-1642 UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India. Ms. M. Varunapriya https://orcid.org/0009-0006-9580-5726 UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India. Distracted driving is a major global contributing factor to traffic accidents. Distracted drivers are three times more likely to be involved in an accident than non-distracted drivers. This is why detecting driver distraction is essential to improving road safety. Several prior studies have proposed a range of methods for identifying driver distraction, including as image, sensor, and machine learning-based approaches. However, these methods have limitations in terms of accuracy, complexity, and real-time performance. By combining a convolutional neural network (CNN) with the You Only Look Once (YOLO) object identification method, this study suggests a unique way to driver distraction detection The two primary phases of the suggested paradigm are object identification utilizing Yolo and classification of the identified things. The YOLO algorithm is used to identify and pinpoint the driver's hands, face, and any other objects that might draw their attention away from the road. The objects that have been observed are then categorized using a CNN to determine whether or not the driver is distracted. When evaluated on a publicly available dataset, the proposed model shows good performance in detecting driver preoccupation. Utilize the CNN algorithm in addition to ocular features to determine the driver's level of fatigue. The proposed method might be incorporated into advanced driver assistance systems with real-time environment to improve road safety. 05 30 2024 18 22 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijitee.F9888.13060524 https://www.ijitee.org/portfolio-item/F988813060524/