Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm
The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and tr...
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40027/ http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf |
| _version_ | 1848825930534879232 |
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| author | Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, M. Saef Ullah |
| author_facet | Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, M. Saef Ullah |
| author_sort | Beg, Mohammad Sojon |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and traffic lamps. The dataset consists of the driving distance from Hospital Pekan to the University of Malaysia Pahang. Around one thousand images were selected in Roboflow for the train dataset. The model utilises the YOLO V8 deep learning algorithm in the Google Colab environment and is trained using a custom dataset managed by the Roboflow dataset manager. The dataset comprises a diverse set of training and validation images, capturing the unique characteristics of Malaysian roads. The train model's performance was assessed using the F1 score, precision, and recall, with results of 71%, 88.2%, and 84%, respectively. A comprehensive comparison with validation results has shown the efficacy of the proposed model in accurately detecting vehicles, motorcycles, and traffic lamps in real-world Malaysian road scenarios. This study contributes to the improvement of intelligent transportation systems and road safety in Malaysia. |
| first_indexed | 2025-11-15T03:36:45Z |
| format | Article |
| id | ump-40027 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:36:45Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-400272024-01-16T04:59:20Z http://umpir.ump.edu.my/id/eprint/40027/ Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, M. Saef Ullah TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics The issue of road accidents endangering human life has become a global concern due to the rise in traffic volumes. This article presents the evaluation of an object detection model for University of Malaysia Pahang (UMP) roadside conditions, focusing on the detection of vehicles, motorcycles, and traffic lamps. The dataset consists of the driving distance from Hospital Pekan to the University of Malaysia Pahang. Around one thousand images were selected in Roboflow for the train dataset. The model utilises the YOLO V8 deep learning algorithm in the Google Colab environment and is trained using a custom dataset managed by the Roboflow dataset manager. The dataset comprises a diverse set of training and validation images, capturing the unique characteristics of Malaysian roads. The train model's performance was assessed using the F1 score, precision, and recall, with results of 71%, 88.2%, and 84%, respectively. A comprehensive comparison with validation results has shown the efficacy of the proposed model in accurately detecting vehicles, motorcycles, and traffic lamps in real-world Malaysian road scenarios. This study contributes to the improvement of intelligent transportation systems and road safety in Malaysia. Semarak Ilmu Publishing 2024-03 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf Beg, Mohammad Sojon and Muhammad Yusri, Ismail and Miah, M. Saef Ullah (2024) Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (1). pp. 38-50. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.34.1.3850 10.37934/araset.34.1.3850 |
| spellingShingle | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Beg, Mohammad Sojon Muhammad Yusri, Ismail Miah, M. Saef Ullah Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title | Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title_full | Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title_fullStr | Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title_full_unstemmed | Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title_short | Evaluating the Performance of a Visual Support System for Driving Assistance using a Deep Learning Algorithm |
| title_sort | evaluating the performance of a visual support system for driving assistance using a deep learning algorithm |
| topic | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/40027/ http://umpir.ump.edu.my/id/eprint/40027/ http://umpir.ump.edu.my/id/eprint/40027/ http://umpir.ump.edu.my/id/eprint/40027/1/Evaluating%20the%20Performance%20of%20a%20Visual%20Support%20System%20for%20Driving%20Assistance%20using%20a%20Deep%20Learning%20Algorithm.pdf |