Beg, Mohammad Sojon and Muhammad Yusri, Ismail and Miah, Md Saef Ullah and Mohamad Heerwan, Peeie (2023) Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31 (1). pp. 226-236. ISSN 2462-1943. (Published)
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Abstract
One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective is to setup a driving support system for detecting vehicles, motorcycles, and traffic signals on the roads near to Universiti Malaysia Pahang using object detection techniques. The video was taken through a direct camera to capture video footage of traffic objects on the roads in the district, which was then analysed using the YOLO-V8 deep learning algorithm. The system was trained on a primary dataset of 1,068 images, with 70% of the dataset used for training, 20% for testing and 10% for validation. After conducting a performance validation, the system achieved a mean average precision (mAP) of 88.2% on train dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. The results of this study could be beneficial for road safety authorities and researchers interested in developing intelligent transportation systems.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Object detection; Deep Learning; Yolo-V8; driving assisting; image processing |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Institute of Postgraduate Studies Centre of Excellence: Automotive Engineering Centre Centre of Excellence: Automotive Engineering Centre Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 16 Oct 2023 03:57 |
| Last Modified: | 16 Oct 2023 03:57 |
| URI: | http://umpir.ump.edu.my/id/eprint/38883 |
| Download Statistic: | View Download Statistics |
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