Citation

Joseph, Sheemona C. and S., Ganesh and Suriyan, Kannadhasan and K., Selvi Priya (2025) Performance Benchmarking: Pre-trained Models and Custom Convolutional Neural Networks in Deep Learning. Journal of Informatics and Web Engineering, 4 (2). pp. 225-235. ISSN 2821-370X

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Abstract

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, even in challenging scenarios such as low light levels, adverse weather, or high traffic. CNN image processing enables the system to accurately recognize and categorize traffic signs. Real-time predictions made by the CNN model after training aid ADAS and autonomous vehicles in comprehending road conditions. Real-time recognition is essential for tasks like managing turns, stopping at red lights, and adhering to speed restrictions. The research also addresses real-world challenges to ensure the model performs effectively in light or weather changes. A thorough testing process validates the model's accuracy and reliability. Ultimately, this technology might significantly increase road safety by providing drivers with more precise information, improving ADAS and AV decision-making skills, and reducing the number of accidents caused by drivers misinterpreting traffic signals

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	25 Jun 2025 08:20
Last Modified:	25 Jun 2025 08:20
URII:	http://shdl.mmu.edu.my/id/eprint/14018

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