Abstract: Traffic signals play an important role in maintaining road safety by providing important information to drivers about speed limits, potential hazards, and legal guidance. Traffic sign recognition is an important component of intelligent transportation systems aimed at improving road safety and efficiency. In this paper, we propose a deep learning algorithm based on Convolutional Neural Networks (CNNs) to detect traffic signals in real-time scenarios.
The proposed model uses a CNN algorithm to detect and classify traffic signals efficiently from the input images. We present a method that combines image preprocessing and CNN-based algorithms to accurately extract and identify traffic signal features. Through extensive testing and analysis of the dataset, we demonstrate how our model is efficient and robust in recognizing different traffic signals. This research contributes to the advancement of intelligent navigation systems by providing reliable and effective solutions for vehicular signal detection using deep learning techniques.
Keywords: CNN, intelligent transportation systems, traffic sign detection.
Cite:
Prof. Ravindra Mule, Khush Marwadi, Saurabh Mapari, Prem Mohite, Rushikesh Shinde, "Deep Learning Model for Traffic Sign Detection ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 2, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13239.
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DOI:
10.17148/IJARCCE.2024.13239
