CNN-Based Traffic Sign Recognition with Voice Alerts

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Embedded Systems and Emerging Technologies
Received Date: 09/05/2024
Acceptance Date: 10/18/2024
Published On: 2024-11-16
First Page: 1
Last Page: 9

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By: Shilpa Shahapur, Nyamatulla Patel, and Mohammed Ziaullah Choudhari

1-Student, Department of MCA, Secab Institute of Engineering and Technology, Vijayapur, Karnataka, India
2-Associate Professor, Department of MCA, Secab Institute of Engineering and Technology, Vijayapur, Karnataka, India.
3-Associate Professor, Department of Electronics and Communication Engineering, Secab Institute of Engineering and Technology Vijayapur,Karnataka, India.

Abstract

Traffic sign recognition is a crucial component of intelligent transportation systems, significantly enhancing road safety by aiding both autonomous and human drivers. This paper presents a convolutional neural network-based traffic sign recognition system integrated with voice alerts. Our approach utilizes a deep learning model trained on a comprehensive dataset of traffic signs to accurately identify various road signs in realtime. Upon recognition, the system issues corresponding voice alerts to inform the driver, thus providing a hands-free method of ensuring road awareness. The idea for a system is designed in a modular fashion, with distinct phases encompassing data collection, preprocessing, model training, real-time recognition, and voice alerts. An extensive dataset of road signals pictures is collected and pre-processed to standardize the data’s size and quality. A custom convolutional neural network architecture is then developed to extra hierarchical features from the images and performs accurate classification. The convolutional neural network model is meticulously designed to balance high accuracy with computational efficiency, making it practical for real-world applications. The results from experiments indicate the system’s high reliability and effectiveness across various conditions, underlining its potential to reduce traffic incidents and enhance overall traffic management. This integration of convolutional neural network -driven visual recognition with auditory feedback offers a promising advancement in the field of driver assistance system.

Keywords: CNN, accident detection, road signals, visual recognition, vehicles, deep learning, intelligent transportation systems

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Citation:

How to cite this article: Shilpa Shahapur, Nyamatulla Patel, and Mohammed Ziaullah Choudhari, CNN-Based Traffic Sign Recognition with Voice Alerts. International Journal of Embedded Systems and Emerging Technologies. 2024; 10(02): 1-9p.

How to cite this URL: Shilpa Shahapur, Nyamatulla Patel, and Mohammed Ziaullah Choudhari, CNN-Based Traffic Sign Recognition with Voice Alerts. International Journal of Embedded Systems and Emerging Technologies. 2024; 10(02): 1-9p. Available from:https://journalspub.com/publication/cnn-based-traffic-sign-recognition-with-voice-alerts/
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