Volume 21, Issue 4 (3-2025)
JSDP 2025, 21(4): 97-112 |
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Haghparast D, Fotouhi A M. Fatigue and drowsiness detection of the car driver based on image processing and artificial intelligence on the mobile phone. JSDP 2025; 21 (4) : 7
URL: http://jsdp.rcisp.ac.ir/article-1-1377-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1377-en.html
Department of Electrical Engineering, Tafresh University, Tafresh 39518-79611, Iran & Assistant professor of Electrical Engineering Department, Tafresh university, Tafresh, Iran
Abstract: (470 Views)
One of the important factors in traffic accidents is the fatigue and drowsiness of the driver. In this paper, by using the driver's face detection and eye state recognition based on image processing and artificial intelligence, the driver's drowsiness is detected, and appropriate alarms sound to wake up the driver. The proposed method is implemented on the driver's mobile phone and uses the facilities of the phone, including processor, camera, and alarm, so it requires no additional hardware in the car. The method used and implemented in order to detect and determine the position of the face is based on the Hare-Cascade algorithm. In order to further speed up the algorithm by combining the two stages of eye detection and eye state detection, the Hare-Cascade method has been used to detect open eyes in the face area. The proposed algorithm, while providing the necessary accuracy, unlike the existing numerous and advanced algorithms, including algorithms based on deep learning, has a low computational cost and can be implemented in real time on different types of smart mobile phones. Also, by adjusting the sensitivity of the software by the user, based on the detection of one or two open eyes in the area of the face and the time between two consecutive frames of not detecting open eyes, increasing the number of correct alarms and reducing the number of false alarms can be controlled.
In this research to train and increase the accuracy of the intelligent model used, a database of 500 suitable images in different driving situations was prepared and used. Experimental results on 20 test videos in different driving situations show the proper performance of the designed system by creating 95% of the expected alarms. Based on the results of numerous and various experimental tests with the acceptable performance of the product of this applied research in detecting driver drowsiness and creating correct alarms, it seems that if used by drivers, it can prevent many car accidents.
In this research to train and increase the accuracy of the intelligent model used, a database of 500 suitable images in different driving situations was prepared and used. Experimental results on 20 test videos in different driving situations show the proper performance of the designed system by creating 95% of the expected alarms. Based on the results of numerous and various experimental tests with the acceptable performance of the product of this applied research in detecting driver drowsiness and creating correct alarms, it seems that if used by drivers, it can prevent many car accidents.
Article number: 7
Keywords: Driver fatigue and drowsiness, image processing, artificial intelligence, face and eye detection, mobile phone application
Type of Study: Applicable |
Subject:
Paper
Received: 2023/04/26 | Accepted: 2024/12/4 | Published: 2025/03/18 | ePublished: 2025/03/18
Received: 2023/04/26 | Accepted: 2024/12/4 | Published: 2025/03/18 | ePublished: 2025/03/18
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