Paper Title
Traffic Sign Board Recognition And Voice Alert System Using CNN
Article Identifiers
Authors
T.Swapna , M.Tejaswani , E.Kavitha , K.Sukanya , K.Vasantha
Keywords
Traffic Sign Recognition, Convolutional neural network, Voice alert, datasets, object detection, Traffic signs, Computer vision, Deep learning.
Abstract
– In recent years, advancements in computer vision and deep learning have paved the way for various applications in traffic management and safety. This paper presents a novel approach for real-time recognition of traffic signboards using Convolutional Neural Networks (CNNs) and an accompanying voice alert system. The proposed system utilizes a CNN architecture to accurately detect and classify traffic signs from input images captured by a camera mounted on a vehicle. Upon detection, the system generates voice alerts to notify the driver about the recognized traffic signs, enhancing situational awareness and promoting safer driving practices. Experimental results demonstrate the effectiveness and efficiency of the proposed system in accurately recognizing traffic signs across diverse environmental conditions
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How To Cite (APA)
T.Swapna, M.Tejaswani, E.Kavitha, K.Sukanya, & K.Vasantha (April-2024). Traffic Sign Board Recognition And Voice Alert System Using CNN. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), b435-b441. https://ijnrd.org/papers/IJNRD2404156.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : b435-b441
Other Publication Details
Paper Reg. ID: IJNRD_217344
Published Paper Id: IJNRD2404156
Downloads: 000121981
Research Area: Computer EngineeringÂ
Country: Ananthapur, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404156.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404156
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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