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@article{187241,
        author = {Sanket S. Rathod and Tejal Bagul},
        title = {SIGNALYSE: Traffic Signal Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5565-5568},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187241},
        abstract = {The Traffic Signal Detection System (TSDS) is a crucial component in the advancement of autonomous driving and intelligent transportation systems. It enables vehicles or surveillance systems to recognize and interpret traffic lights, thereby ensuring road safety, minimizing human error, and optimizing traffic flow. This paper presents an overview of the methodologies, technologies, and challenges associated with the development of an efficient Traffic Signal Detection System. The increasing demand for intelligent transportation systems (ITS) has led to rapid advancements in traffic signal detection technologies. This research presents a computer vision-based approach to detect and recognize traffic signal lights using deep learning algorithms. The proposed system utilizes convolutional neural networks (CNN) integrated with Open CV for real-time image processing and detection. The YOLO (You Only Look Once) framework is employed to achieve efficient and accurate classification of traffic lights under varying lighting and weather conditions. Experimental results demonstrate the system’s capability to achieve high accuracy in recognizing traffic light states, enabling its integration into autonomous vehicles and smart traffic management systems. The study further discusses limitations and proposes future improvements for scalability and robustness.},
        keywords = {Traffic Signal Detection, Intelligent Transportation System, Machine Learning, Image Processing, Web Application, Real-time detection.},
        month = {November},
        }