Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor
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 Malay...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Semarak Ilmu Publishing
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38883/ http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf |
| _version_ | 1848825624202838016 |
|---|---|
| author | Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, Md Saef Ullah Mohamad Heerwan, Peeie |
| author_facet | Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, Md Saef Ullah Mohamad Heerwan, Peeie |
| author_sort | Beg, Mohammad Sojon |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:31:53Z |
| format | Article |
| id | ump-38883 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:31:53Z |
| publishDate | 2023 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-388832023-10-16T03:57:24Z http://umpir.ump.edu.my/id/eprint/38883/ Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, Md Saef Ullah Mohamad Heerwan, Peeie TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery 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. Semarak Ilmu Publishing 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf 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) https://doi.org/10.37934/araset.31.1.226236 10.37934/araset.31.1.226236 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, Md Saef Ullah Mohamad Heerwan, Peeie Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title | Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title_full | Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title_fullStr | Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title_full_unstemmed | Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title_short | Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor |
| title_sort | enhancing driving assistance system with yolo v8-based normal visual camera sensor |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/38883/ http://umpir.ump.edu.my/id/eprint/38883/ http://umpir.ump.edu.my/id/eprint/38883/ http://umpir.ump.edu.my/id/eprint/38883/1/Enhancing%20Driving%20Assistance%20System%20with%20YOLO%20V8-Based%20Normal%20Visual%20Camera%20Sensor.pdf |