Driver Distraction and Drowsiness Detection Based on Object Detection Using Deep Learning Algorithm
T. Nandhakumar1, S. Swetha2, T. Thrisha3, M. Varunapriya4
1Mr. T. Nandhakumar, Assistant Professor, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India.
2Ms. S. Swetha UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India.
3Ms. T. Thrisha, UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India.
4Ms. M. Varunapriya, UG Scholar, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal (Tamil Nadu), India.
Manuscript received on 30 April 2024 | Revised Manuscript received on 11 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 18-22 | Volume-13 Issue-6, May 2024 | Retrieval Number: 100.1/ijitee.F988813060524 | DOI: 10.35940/ijitee.F9888.13060524
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Convolutional Neural Network, Driver distraction, Drowsiness Detection, Object Detection, Yolo Algorithm.
Scope of the Article: Network Traffic Characterization and Measurements