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@article{196575,
        author = {Kotla Prasanna Laxmi and Kamatham Jonathan Samuel and Patlasingi Rahul and Alluri Sri Lakshmi Prasanna Harshitha and Anantharapu Santosh Kumar},
        title = {Automatic Traffic Sign Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4349-4359},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196575},
        abstract = {Traffic sign detection plays a vital role in intelligent transportation systems and autonomous vehicles by enabling them to recognize and follow road regulations automatically. This project focuses on developing an efficient, real-time system for detecting and classifying traffic signs using deep learning and computer vision techniques. A Convolutional Neural Network (CNN) is trained on a large dataset of traffic sign images to learn distinctive features such as color, shape, and symbols. MobileNet is used for traffic sign classification, while the Single Shot MultiBox Detector (SSD) is used for traffic sign detection, enabling fast and accurate real-time performance on embedded platforms. The proposed system aims to overcome challenges such as poor lighting, weather conditions, and driver distraction, which often lead to missed or misinterpreted signs. By implementing the system on a Raspberry Pi platform, vehicles can make timely and safe decisions, thereby improving road safety and reducing the likelihood of accidents and traffic violations, especially in low-cost vehicles and developing regions.},
        keywords = {Image Steganography, Randomized LSB, AI-Assisted Pixel Selection, Random Forest Regressor, Hybrid Encryption, AES-256, ChaCha20, Secure Communication, Web-Based Steganography.},
        month = {April},
        }