Almashwali, Ayman Ahmed Hashem Salem and Mohamed Kassim, Anuar and Yaacob, Mohd Rusdy and Tan, Kim Loong and Awangku Jaya, Awangku Khairul Ridzwan and Ngadiron, Zuraidah and Yasuno, Takashi (2024) Vision-based road signage recognition for autonomous vehicle in agricultural plantation. International Journal of Agriculture, Forestry and Plantation, 15. pp. 203-209. ISSN 2462-1757
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Abstract
The growth of self-driving vehicles demands dependable vision-based traffic sign recognition systems to maintain safety and efficiency in agricultural plantations. This research aims to create an enhanced traffic sign identification system based on the YOLOv3 algorithm, which will solve the limitations of standard human vision-based approaches, notably in low-light circumstances and with occlusions. The system leverages computer vision and machine learning techniques, requiring extensive training on diverse datasets to ensure robustness against environmental variations and regional signage differences. Implemented on platforms like Google Colab, the system was trained and tested using a comprehensive dataset, achieving a mean average precision (mAP) of 96.96%, precision of 94%, and recall of 95%. Despite its high accuracy and effective real-time processing capabilities, challenges like handling similar signs and occlusions persist. Future work will concentrate on increasing the dataset, refining the model, enhancing occlusion management approaches, and allowing real-time processing on edge devices like the Jetson Nano and Raspberry Pi, boosting system dependability and developing autonomous driving technologies.
Item Type: | Article |
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Uncontrolled Keywords: | Vision-based, Road sign recognition, Precision agriculture, Autonomous vehicle |
Divisions: | Faculty Of Electrical Technology And Engineering |
Depositing User: | Sabariah Ismail |
Date Deposited: | 23 May 2025 16:32 |
Last Modified: | 23 May 2025 16:32 |
URI: | http://eprints.utem.edu.my/id/eprint/28700 |
Statistic Details: | View Download Statistic |
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